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- 1. 1 Children Hunger Walid Alghamdi & Rakan Alharbi TABLE OF CONTENTS Introduc*on 2 ............................................................................................... ................................................... Methodology 3 ............................................................................................... ................................................. Key Findings 4 .............................................................................................. . ............................................... Hunger and lack of educa*on 4 ............................................................................................... .................... Lack of nutri*ons 4 ............................................................................................... ....................................... Suppor*ng chari*es 4
- 2. ............................................................................................... .................................. Visual Data 5 ............................................................................................... ..................................................... Conclusion 5 ..................................................................................... .......... ..................................................... 2 INTRODUCTION Imagine you have a kid that starving and asking for food, but you cannot buy this food! This is what poor kids face. There are so many organiza=ons trying to help those kids to reduce the number of kids who die every year because of hunger. For example, the children’s hunger’s project in Brevard County provide poor kids with enough food during weekends, so they can study and interact in schools. Children’s hunger use technology which makes them beEer than other chari=es that require to get into the charity place and pay. Unfortunately, hungry kids are not something new;
- 3. Since 1984, No Kid Hungry were officially help hungry kids to live which means there are kids faced hunger before 1984. In 2018, the level of poverty level was $25,750 for a family of 4 members (the U.S. Department of Health and Human Services); this is a very high number and that’s why people should start thinking about those kids because they are the future of this country. Something might be weird, but some wealthiest families have insecurity of food and they think it is not a big issue. However, studies show that children who suffer from hunger will do poorly in their study (Lancaster University, 2019) 3 P R O D U C T / S E R V I C E / METHODOLOGY In this research we were commiGed to obtain the data from reliable source. Such as American Psychological Associa*on, World Hunger News and Habitat for Humanity of Brevard County. APer the
- 4. process of collec*ng data, the team reviewed, and filtered them as applicable. In an aGempt to be accurate and credible; the team decided to eliminate any informa*on that might risk the trustworthiness and truthfulness of the research. Addi*onally, the informa*on that we took from our sources were peer reviewed and most resent. We will provide the audience with what they should know about the issue, so people who does not have *me they will get the idea real quick. Also, we provide interested poten*al donators who want to read more. Donators will read sta*cs and how the problem can be solved not showing the problem only. Furthermore, if they have any other solu*ons, they will be able to provide them to us. 4 KEY FINDINGS Hunger & lack of Education The issue of hunger is indirectly affected by lack of educa*on; These days, jobs are now hard to get because of the requirements and the number of people applying for the
- 5. same job. Without higher educa*on, people cannot get a job with a high salary. Unemployed people; cannot afford the price of their daily food, and it is even harder for those who have a family with kids. According to Mercy Corps organiza*on, around nine million humans die every year because of hunger and disease related to hunger ("Quick facts: What you need to know about global hunger," 2020). So, if the individual is not able to buy food, she/he would suffer from hunger or the disease that comes with it. Kids have a higher chance of dying more than adults because of their limited endurance. Kids make up around three million out of nine million dying because of hunger (UNICEF, 2018a). Lack of nutritions Children's hunger is noted by malnutri4on. Malnutri4on greatly inhibits cogni4ve development and makes them unproduc4ve. Hunger is both a cause of poverty and may result from poverty. Despite efforts to curb hunger, global childhood hunger is s4ll widespread. According to the world health organiza4on, in 2016, an
- 6. es4mated 5.6 million children below the age of five died (Lam). Much of the malnutri4on problem occurs in Africa whose rate is 8 4mes the rate of malnutri4on deaths in Europe. 5 Supporting charity organizations Habitat for Humanity of Brevard County is a non-profit organiza4on that is Chris4an based (Brevard). It offers low-income families with decent, affordable and safe homes. In addressing children's hunger, the organiza4on helps low-income families with homes to prevent children's deaths that arise from poverty and living in unhealthy environments (Humanity). The assump4on is that if parents have a decent home they can be able to feed their children with healthy meals and adequate amounts of food to prevent malnutri4on and under-nutri4on. The organiza4on works with volunteers who supply them with cash and in-kind dona4ons that are channeled to the organiza4on programs and beneficiary families. The organiza4on is commiSed
- 7. to figh4ng poverty which is the leading cause of childhood hunger. 6 Additional Data 7 4% 7% 89% Food Secure house holds House holds with low food security House holds with very low food security CONCLUSION That’s what hungry kids ask for, and they die every year with a number of 3 millions. What if your kid was one of them. How will you react? All what people’s needs to save the future of the U.S is to save those poor people because they are the only people who will help to raise their country. A minimum of $10 dona*on monthly to charity, will save a kid life thus a Country's future.
- 8. 8 IntroductionProduct/Service/MethodologyKey FindingsHunger & lack of EducationLack of nutritionsSupporting charity organizationsAdditional DataConclusion Answer_SheetDO NOT WRITE IN THIS TAB!!!!!!!Question SheetPartPointsAnswerQ11A2DQ11B2AQ11C2BQ11D2BQ11E2 CQ11F2BQ11G2DQ11H2CQ11I8Nothing to SubmitQ22A288.7051701852Q22B222.5853729642Q22C222271 203.5555556Q22D223491893.0332999Q22E284.0200005Q22F2 13028700Q22G41.4572451129Q22H4SECONDQ33A20.035355 3391Q33B25.6568542495Q33C40.0000000077Q33D4REJECTQ 33E40.0000000154Q33F22.5260074194Q33G20.4206044555Q3 3H20.3085375387Q33I40.0145398585Q33J40.0018284237Q33 K2495Q33L42400.9117629338Q33M2NQ33N40.718256966Q33 O40.0949128951Q44A2- 457129.583350942Q44B49492.8404176904Q44C2- 1.806835499Q44D49242.7902357367Q44E2LOGQ44F2SAMEQ 44G612591.2479687789Q44H20.3034774094Q44I2NQ44J2YQ4 4K20.0049174795Q44L2FEATQ44M20.5290309924Q44N2YQ4 4O212438.8917062984Q44P2- 0.064644784Q44Q20.5394134353Q44R2YTotal134 InstructionsInstructionsAnswer all the questions on the tabs that say Q on them. Put all answers in the red shaded cells in the Q sheets.Follow the directions on the Q tabs AND put your answers in the red cells there as indicated.You may create new tabs for work if you wish. I will not read them. I try to not read any of the exams. That is why I write a python program to grade them.Answers should have 3 significant figures (3 numbers) in them. More is OK, as the computer is reading it.I have used decimals for all %'s. Use decimals in your answers.Do not do anything to the Answer_Sheet tab.I will auto grade the exams using a python script.Answer as a number with no special characters (, . ; for example) or you will lose points. Only answer with text if the question indicates to do so in the Notes column.Use decimals in your answer unless otherwise
- 9. specified.Failure to follow directions will result in loss of points.Failure to uplaod your correctly named file to canvas will result in loss of points. (see question 1H)Notes DataZoom Q2 on returns:DateAdj CloseVolumeReturn FirstReturn Second4/15/1962257647000.2264516291.00354017624/22/1966. 220001271004004/29/1979.18143529005/6/1979.629997919580 05/13/1989.980003152733005/20/1976.25136420005/27/1979.7 3000367437006/3/1994.050003165130006/10/19100.290001174 553006/17/19100.470001124268006/24/1988.790001186835007 /1/1991.879997124816007/8/1993.30000398843007/15/1993.37 9997120531007/22/19102.19999799194007/29/1993.660004100 280008/5/1994.19999755253008/12/1992.52999955513008/19/1 991.62999749835008/26/1991.66999847094009/2/1985.410004 162211009/9/1978.849998149125009/16/1982.62999793221009 /23/1976.04000180139009/30/1976.529999852860010/7/1971.3 499981075290010/14/1966.0800022717230010/21/1963.450001 1660560010/28/1970.3899991697200011/4/1969.849998729700 011/11/1970.010002817980011/18/1973.1600041034030011/25/ 1974.5564140012/2/1962.7400022258550012/9/1963.54000118 62210012/16/1966.931163270012/23/1966.639999456910012/3 0/1967.27999949575001/6/2073.089996170464001/13/2075.559 998135758001/20/207360475001/27/2076.30000386694002/3/2 088.639999338086002/10/2090.949997121760002/17/20101.76 0002302144002/24/20105676890003/2/20114.32844188003/9/2 0107.470001587234003/16/20130.550003796475003/23/20151. 699997873504003/30/20128.199997767911004/6/20124.510002 873014004/13/20150.259995562082004/17/20152.34919783633 92 OJ_DataLOGS:Second Regression with fewer variables:weeksales1sales2sales3sales4sales5price1price2price3 price4price5disp1disp2disp3disp4disp5feat1feat2feat3feat4feat5 grmar1grmar2grmar3grmar4grmar5LOGsales1LOGsales2LOGsa les3LOGsales4LOGsales5LOGprice1LOGprice2LOGprice3LOG price4LOGprice5feat2disp2LOGprice1LOGprice2feat240652884 4847040422424320.0570.0600.0300.0470.0250.284313725500.4
- 27. typing the corresponding letter in the Answer Column.Question NumberPointsQuestionABCDEAnswer:1A2Consider 2 events, A and B. A = getting an A in this class. B = getting a B in this class. Which of the following is true?A and B are independent events.A and B are mutually exclusive events.A and B are not independent events.B and CNone of the aboveDThe events are both mutually exclusive and not independent. You cannot get both an A and a B, so they rae mutually exclusive. If you get an A, then you know you did NOT get a B, so they are not independent.1B2In a symmetric distribution, which of the following is true?median = meanmedian > meanmedian < meanNot enough information given.None of the aboveAIn a symetric distribution, the mean = the median.1C2The null hypothesis, H not, should beWhat you want to show.The opposite of what you want to show.Unrelated to what you want to show.Not enough information given.None of the aboveBThe null hypothesis should be the opposite of what you want to show. 1D2The Central Limit Theorem states the following about sample statistic distributions:for small sample sizes, the sample statistic distribution will be normally distributed.For large sample sizes, the sample statistic distribution will be normally distributed.The sample statistic distribution will look similar to the population distribution.The sample statistic standard deviation increases as the sample size increases.None of the aboveBAnswer B is basically a restatement of the Central Limit Theorem.1E2Which of the following best measures accuracy of a regression model?p-valuesresidual plotsR^2 or adjusted R^2t- statisticNone of the aboveCR^2 (simple regression) or Adjusted R^2 (multiple regression) tells us about accuracy of the model.1F2If your data is growing exponentially over time, the best way to display it isA line graph using a linear scaleA line graph using a log scaleA scatterplot using a linear scaleA scatterplot using a log scale.None of the aboveBA line graph is required because the data is linked over time. A log scale is better for exponential growth because it makes exponential growth appear linear.1G2If you run a regression and your R^2 =
- 28. 0.9 and your p-values are very low, thenYour model is ready to use.Your model is not good based on the low p-values.Your model is not good based on the high R^2. You must look at the residual plots to draw any conclusions.None of the aboveDYou must look at residual plots. All 3 diagnostic tools must be good before you should use the model (R^2, p-values, and residual plots).1H2If you try 100 models, how many do you expect to be significant at the 5% level simply due to random chance?01005Not enough information given.None of the aboveCYou expect 5% of the models to appear good at the 5% level due to random chance. This means 5% of 100 = 5.1I8Upload your file to canvas with the correct name. This is worth 6% of the final exam grade. Nothing to submit.Total24 Work_Q1Work can be done on this page for Q1 Q2Question 2: Shorter ProblemsQuestionPointsInstructionsUse the data from the Data tab to answer the following questions. The data are weekly adjusted closing prices and volumes for the stock of Zoom (the conferencing software). QuestionsNotes:Answer Guide:2A2Average Weekly Adjusted Close of ZM.88.7051701852Use formula in Excel2B2Weekly standard deviation of ZM Adjusted Closes22.58537296422C2Average Weekly Volume of ZM22271203.55555562D2Weekly standard deviation of ZM23491893.03329992E2Median Weekly Adjusted Close of ZM84.02000052F2Median Weekly Volume of ZM130287002G4What percentage return has ZM generated over this time period?1.4572451129Use (final - initial)/initial to compute percentage return.146% increase!2H4Did ZM generate more of these returns in the FIRST or SECOND half of the data set?SECONDAnswer FIRST or SECONDEasiest way to answer is to compute for first and second half returns. Then compare. Second half return is almost 100%, first half return is much lower. Work done on data tab.Total20 Work_Q2Work can be done on this page for Q2 Q3Question 3: Longer ProblemsAssumptions and Information:QuestionsQuestion NumberPointsHuman Resources
- 29. and Sick DaysAnswers:Notes:Answer Notes:You work as an analyst at a large firm and are planning schedules for the upcoming month (pretend there is no Covid-19). From examining the entire population of workers, you know that an average employee misses 2.5 days of work each month with a standard deviation of 0.5. You may assume all months are identical for your business (stupid assumption in the real world, but it makes this problem much easier). You study a sample of 200 employees and see that on average they missed 2.7 days each last month. 3A2What is your best guess for the standard deviation of sample statistic for this 200 person sample?0.0353553391Use Central Limit Theorem Formula, sigma/square root nYour boss is concerned that people are missing more days of work than they did in the past. Your sample of 200 will be used to test this. 3B2What is the z-score associated with your measured mean from the sample?5.6568542495Use z-score formula: (X-mu)/sigmaNull is that people are missing the same or fewer days.3C4What is the p-value associated with the observed mean from your sample?0.0000000077Draw picture. You want area to right of 5.6 z-score. You could also calculate this area on the raw variables by feeding the norm.dist command that info.3D4Based on your sample of 200, would you accept or reject the null hypothesis at the 1% level?REJECTAnswer Accept or Rejectreject, as p-value is less than alphaSuppose now that your boss is concerned that people are missing more or fewer days than in the past.3E4What is the p-value associated with your sample under the new null?0.0000000154Double the old p- value, as now we are considering extreme values on both sides (2-Tailed)Assume that people are missing days at the same rate as the entire population of workers from the beginning of the question.3F2What is the upper bound of the 90% confidence level for a sample of 1000 workers on average days missed?2.52600741940.0260074194CONFIDENCE.NORM gives us the 'garbage' term, or the +- term away from the middle. So add that garbage term to the middle (mean) to get
- 30. upper bound.Data Analysis and ProbabilityAnswers:Suppose you work for the Centers for Disease Control in the US. From studying the data, you conclude that the number of deaths each day is drawn from a normal distribution with mean 2000 and standard deviation 500. 3G2What is the probability that deaths tomorrow are between 1900 and 2500?0.4206044555Draw a picture. You want the area between 1900 and 2500. Formulas at left take area left of 2500 and subtract area left of 1900, leaving you the area in between.3H2What is the probability that deaths tomorrow are below 1750?0.3085375387Simpler question, same logic.Suppose now you are drawing cards from a standard deck.3I4What is the probability of drawing at least 5 red cards if you draw 6 cards from a standard 52 card deck without replacement?0.0145398585answer = probability of getting 5 red cards + probability of getting 6 red cards = 26 choose 5/52 choose 6 + 26 choose 6/52 choose 6. Excel uses COMBIN for the choose.Suppose you draw 4 cards without replacement.3J4What is the probability of drawing exactly 4 face cards (Jack, Queen, King, or 3 for every suit, 12 total)0.001828423712 choose 4/52 choose 43K2How many combinations of exactly 4 face cards are there in a standard deck?49512 choose 4 = all the ways you can combine 12 cards into groups of 4These questions deal with election polls.Election PollsSuppose you work for Joe Biden's presidential campaign. You have been tasked with answering the following quantitative questions about his campaign polls. You may assume there are only two outcomes, Y for Joe or N for Joe.3L4How many potential voters do you need to sample to provide a 2% margin of error at the 95% confidence interval?2400.9117629338See lecture 9 slies for formula deriviation. Derived from the formula for the error term or 'garbage'. You change error to known and sample size to the unknown and use algebra to solve for sample size. Use 0.5 as p and p' as that is the worst case scenario. N = Zalpha^2 p'(1- p')/e^2Use NORM.INV of 0.025 to get Zalpha-1.9599639845A rival analyst challenges your answer to 3L by saying that rather
- 31. than sampling a certain number, you must sample a certain percentage of the population to have a good guess at who will win the election. 3M2Does the rival analyst's claim have any merit?NAnswer Y, NIt does not matter what portion of the population you sample. It is the sample size that determines the error (statistical sampling error).Suppose you run a poll of 300 voters in Texas and find that 155 support Biden. 3N4Using only your poll, what is the probability that Biden will win Texas? (assume you must win 50% of the vote to win). 0.718256966Use proportions formulas from class. Technicallly speaking, we are using the normal distribution to approximate the binomial distrbution. For sigma, use CLT version for proportions and plug in 155/300 for psigma = (155/300*(1- 155/300)/300)^0.5What is the margin of error associated with the poll in 3N?3O4What is the margin of error associated with the poll in 3N at the 90% confidence level?0.0949128951Answer in decimal form, giving the width of the confidence interval. e = Zalpha * sigma (where sigma already has square root of n in it). Note I asked for width of the interval, so I made the answer positive. I also doubled the error, as the width of the interval is twice the error.- 1.6448536270.0288514715Total46 Work_Q3Work can be done on this page for Q3 Q4Question 4: Working with OJ DataQuestionPointsInstructionsUse the information on the 5 SKU's from the OJ_Data tab to answer these questions. The Data Description is posted on canvas if you need to review it.QuestionsAnswerNotes:Answer Notes:Regress sales2 on price2.4A2What is the slope coefficient on price2?- 457129.583350942See regression output4B4Using the model generated by the regression, forecast sales if price = 0.055.9492.8404176904Y = mx + b2Regress LN(sales2) on LN(price2)4C2What is the slope coefficient on LN(price2)?- 1.806835499See regression output4D4Using the model generated by the regression, forecast sales if price = 0.055.9242.7902357367EXP(Y=mx+b)4E2Which model has a
- 32. better R^2?LOGAnswer LOG, LINEAR4F2Which model as better residual plots?SAMEAnswer LOG, LINEAR, SAMEReally both residual plots look fine, so I added an answer 'SAME'Regress LN(unit sales of SKU2) on LN of all SKU prices and marketing strategies (feat and disp) of SKU2. Do NOT LN feat2 and disp2. (7 Right Hand Side variables).4G6Use this regression to forecast the unit sales of SKU2 when price2 = 0.055, all other prices equal their historical average, and SKU2 is both featured and displayed that month. 12591.2479687789See work on REG_2_LOG_MULT sheet4H2Based on this model, what % change in sales would you expect if you feature SKU2?0.3034774094It is a 30% change.4I2Are the signs of all of the regression coefficients sensible?NAnswer Y, NMany are wrong, see highlighting.4J2Do the relative magnitudes of the coefficients on LN(prices) make sense?YAnswer Y, NYes, as the coefficient on OWN price (price2) is largest4K2Based on this model, what % change to sales would you expect if you raised the price of SKU3 by 10%?0.00491747951/2 of 1% increase in sales of SKU2 when you increase price of SKU34L2Assuming feature and display cost the same amount to implement, which is more cost- effective based on this model?FEATAnswer FEAT, DISPFEAT has higher coefficient, so it affects sales by more.4M2What is the R^2 of your regression?0.5290309924You MUST answer adjsuted R^2 here as it is a multiple regression. This is how the language is used in the real world. Improve your model by removing the variables with bad p-values to create a new model. You need to run a new regression.4N2Are the signs of all of the regression coefficients sensible?YAnswer Y, NRerun the regression dropping all the variables with high p-values. Those are highlighted in red in the REG_2_LOG_MULT sheet.4O2Use this regression to forecast the unit sales of SKU2 when price2 = 0.055, all other prices equal their historical average, and SKU2 is both featured and displayed that month. 12438.8917062984See work on REG_2_LOG_MULT_2 sheet. Note that we dropped display from the model, so you do not use
- 33. it in the prediction.4P2Based on this model, what % change to sales would you expect if you lowered the price of SKU1 by 20%?-0.0646447846.5% drop. This makes sense, as the calculated elasticity is roughly 1/3 (0.32). 1/3 of 20% is roughly 6.5%.4Q2What is the R^2 of this new model?0.53941343534R2Are all of the p-values for this new model good?YAnswer Y, NTotal46 REG_2_LINSUMMARY OUTPUTRegression StatisticsMultiple R0.5945602386R Square0.3535018773Adjusted R Square0.3478308412Standard Error3566.3962422843Observations116ANOVAdfSSMSFSignifi cance FRegression1792845370.931884792845370.93188462.33461877 870Residual1141449986765.895712719182.1569799Total11522 42832136.82759CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept34634.96750199222915.760180787411.8785377 9271.14621392917241E- 2128858.869041107740411.065962876728858.86904110774041 1.0659628767price2-457129.58335094257899.4814074441- 7.89522759510-571828.010075885-342431.156625998- 571828.010075885-342431.156625998RESIDUAL OUTPUTObservationPredicted sales2Residuals17064.33950613861383.660493861427064.3395 0613861287.660493861437064.3395061386711.6604938614470 64.3395061386903.660493861457064.3395061386231.6604938 61467064.3395061386135.660493861477064.3395061386903.6 60493861487064.3395061386135.660493861497064.339506138 64647.6604938614107064.33950613863303.6604938614117064 .339506138639.6604938614127064.3395061386- 632.3395061386137064.3395061386135.6604938614147064.33 950613863111.6604938614157320.8021848786- 1176.8021848786167368.8485154591271.1514845411712778.4 5929802536133.5407019747188492.8694541103- 1772.8694541103198492.8694541103243.1305458897208492.8 694541103723.1305458897218492.86945411034083.130545889
- 36. 1517.193701100611310445.19370110061650.806298899411410 445.19370110062610.806298899411510445.1937011006- 941.193701100611615635.51919396873372.4808060313 price2 Residual Plot 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 6.0312499999999998E-2 5.9751471600000002E-2 5.9646367200000001E-2 4.7812500000000001E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.7187500000000002E-2 5.30208333E-2 4.1562500000000002E-2 4.1562500000000002E-2 5.30208333E-2 5.30208333E-2 3.73958333E-2 3.73958333E-2 3.73958333E-2 5.30208333E-2 5.30208333E-2 5.30208333E-2 5.30208333E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.4687499999999998E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.1562500000000002E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.6770833300000002E-2 4.6770833300000002E-2 4.94791667E-2 3.9479166699999999E-2 3.9479166699999999E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.5729166699999997E-2 4.5729166699999997E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.8700073300000001E-2 4.7812500000000001E-2 4.8460474199999999E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.3780244000000003E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2
- 37. 4.1562500000000002E-2 4.1562500000000002E-2 4.4039505600000001E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.7559436599999998E-2 4.5729166699999997E-2 4.6666666699999998E-2 4.94791667E-2 3.70833333E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.94791667E-2 4.6770833300000002E-2 4.7500000000000001E-2 4.94791667E-2 4.94791667E-2 4.1562500000000002E-2 4.9002420400000003E-2 4.7395833300000002E-2 4.94791667E-2 5.0724032299999999E-2 5.2916666699999997E-2 5.2916666699999997E-2 5.2916666699999997E-2 5.2916666699999997E-2 5.2916666699999997E-2 4.1562500000000002E-2 1383.6604938614328 1287.6604938614328 711.66049386143277 903.66049386143277 231.66049386143277 135.66049386143277 903.66049386143277 135.66049386143277 4647.6604938614328 3303.6604938614328 39.660493861432769 -632.33950613856723 135.66049386143277 3111.6604938614328 - 1176.802184878612 1271.1514845410384 6133.5407019746635 -1772.8694541102595 243.13054588974046 723.13054588974046 4083.1305458897405 -1484.8694541102595 - 893.57606664350169 12300.480806031279 396.48080603127892 -989.57606664350169 4386.4239333564983 1179.7741934980331 - 7652.2258065019669 -4676.2258065019669 - 1661.5760666435017 -797.57606664350169 - 893.57606664350169 -317.57606664350169 - 1648.5766438694482 -2704.5766438694482 - 4432.5766438694482 -4432.5766438694482 - 5488.5766438694482 -2974.9892459970324 - 5968.5766438694482 -5104.5766438694482 -
- 38. 1744.5766438694482 -5200.5766438694482 1548.4808060312789 -5075.5191939687211 - 3952.5766438694482 -3856.5766438694482 - 2608.5766438694482 3737.3640374131137 - 5286.6359625868863 -784.57664386944816 4532.1275226211328 -2859.8724773788672 - 5104.5766438694482 -3664.5766438694482 559.42335613055184 1231.4233561305518 4125.1874185645174 -3650.8125814354826 - 880.57664386944816 -1456.5766438694482 - 496.57664386944816 175.42335613055184 - 880.57664386944816 -2196.7232852029192 2197.5407019746635 -2018.2511219571788 - 2608.5766438694482 367.42335613055184 - 976.57664386944816 -1840.5766438694482 4140.4808060312789 354.27719673032698 943.42335613055184 271.42335613055184 175.42335613055184 -1072.5766438694482 - 496.57664386944816 2604.4808060312789 1068.4808060312789 -4519.2066560827734 463.42335613055184 17935.423356130552 - 3664.5766438694482 -976.57664386944816 - 2608.5766438694482 -1552.5766438694482 - 3280.5766438694482 -2238.1420646287115 4221.1874185645174 2441.7464029560251 367.42335613055184 4972.9211987008639 2383.4233561305518 3247.4233561305518 2479.4233561305518 3055.4233561305518 - 496.57664386944816 -304.57664386944816 - 1552.5766438694482 3449.3640374131137 1670.6877071774943 -400.57664386944816 - 304.57664386944816 11244.480806031279 - 618.51148135255062 -1352.9299729925478 559.42335613055184 -1367.5117508135299 306.80629889940974 -1517.1937011005903 1650.8062988994097 2610.8062988994097 -
- 39. 941.19370110059026 3372.4808060312789 price2 Residuals REG_2_LOGSUMMARY OUTPUTRegression StatisticsMultiple R0.6325873221R Square0.40016672Adjusted R Square0.3949050246Standard Error0.2580246141Observations116ANOVAdfSSMSFSignifican ce FRegression15.06334518555.063345185576.05280935110Resid ual1147.58974396960.0665767015Total11512.6530891551Coef ficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept3.89101349150.6223695586.25193414680.00000 000732.65810419065.12392279242.65810419065.1239227924L OGprice2-1.8068354990.2071862758-8.72082618510- 2.2172699156-1.3964010824-2.2172699156- 1.3964010824RESIDUAL OUTPUTObservationPredicted LOGsales2Residuals18.96499766830.076687337628.964997668 30.065258641838.9649976683- 0.006200322248.96499766830.01819113158.9649976683- 0.069916136668.9649976683- 0.083161363378.96499766830.01819113188.9649976683- 0.083161363398.96499766830.4033715679108.96499766830.28 14817503118.9649976683-0.0965843836128.9649976683- 0.1959568574138.9649976683- 0.0831613633148.96499766830.2627896173158.9818835566- 0.2586522818168.98506462610.0790932357179.384639310.462 9126102189.0611286094- 0.2482851758199.06112860940.0140790886209.06112860940.0 675677736219.06112860940.3784169053229.0611286094- 0.2063209767239.1978164838- 0.0383484422249.63775671450.5999147441259.63775671450.0 445852894269.1978164838- 0.0485008137279.19781648380.4034843101289.82862906270.0 087186873299.8286290627-0.629551883309.8286290627- 0.3664410713319.1978164838-0.1226087858329.1978164838-
- 41. 3227288240.02911111021009.3227288240.04564041221019.32 2728824- 0.06703275031029.42443905920.29896443151039.3964874541 0.19174125821049.3227288240.03740991311059.3227288240.0 4564041221069.63775671450.56138108011079.34022262960.0 1991610751089.4004541649- 0.04031542791099.3227288240.11681669071109.2778323914- 0.05952384981119.20136974820.08147731461129.2013697482- 0.10442206361139.20136974820.19926035021149.2013697482 0.2756333291159.2013697482- 0.04190170661169.63775671450.2148585077 LOGprice2 Residual Plot -2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8175614591851099 -2.8193220342207224 -3.040468 1673953826 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.9370703615340887 -3.1805569605660642 - 3.1805569605660642 -2.9370703615340887 - 2.9370703615340887 -3.2861959898590136 - 3.2861959898590136 -3.2861959898590136 - 2.9370703615340887 -2.9370703615340887 - 2.9370703615340887 -2.9370703615340887 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.1080614585279105 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.1805569605660642 -3.1805569605660642 - 3.006203572747602 -3.006203572747602 -
- 42. 3.006203572747602 -3.0624954904263681 - 3.0624954904263681 -3.006203572747602 - 3.2319821715291743 -3.2319821715291743 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.085018963651827 -3.085018963651827 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.0220747437612556 - 3.0404681673953826 -3.0270067782516215 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.1805569605660642 -3.1285726135817997 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.1805569605660642 - 3.1805569605660642 -3.1226681933487099 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.0457750533188719 - 3.085018963651827 -3.064725144326657 - 3.006203572747602 -3.2945876475027736 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.0624954904263681 - 3.0470255679415414 -3.006203572747602 - 3.006203572747602 -3.1805569605660642 - 3.0158855861730811 -3.0492209592082737 - 3.006203572747602 -2.9813554708302528 - 2.9390369292475245 -2.9390369292475245 - 2.9390369292475245 -2.9390369292475245 - 2.9390369292475245 -3.1805569605660642 7.6687337638087882E-2 6.5258641814464724E-2 - 6.2003221676807385E-3 1.8191130956479284E-2 -
- 43. 6.9916136553787922E-2 -8.3161363303808855E-2 1.8191130956479284E-2 -8.3161363303808855E-2 0.40337156789313688 0.28148175028410094 - 9.6584383635949678E-2 -0.19595685744915237 - 8.3161363303808855E-2 0.26278961727194883 - 0.25865228178577482 7.9093235711795273E-2 0.46291261022955332 -0.2482851758411222 1.4079088626369085E-2 6.7567773577355439E-2 0.37841690531067051 -0.20632097674208971 - 3.8348442223355761E-2 0.5999147441480428 4.4585289393305416E-2 -4.8500813687374134E-2 0.40348431005568308 8.7186873322657732E-3 - 0.62955188300184517 -0.36644107128056902 - 0.12260878584109669 -2.8298106369854636E-2- 3.8348442223355761E-2 2.0492057799577523E-2 - 7.6249405415232729E-2 -0.18366965403606983 - 0.38893278007243026 -0.38893278007243026 - 0.53887292736334658 -0.18024714162421418 - 0.61524590614791919 -0.48171451352339645 - 8.5551798077545982E-2 -0.4957007554981363 0.11397728281730579 -0.37292815723103345 - 0.32756383369613928 -0.31572937604913598 - 0.17341315386888034 0.31605886486472912 - 0.44125025991250233 3.7933022583036546E-3 0.22730212661640614 -0.20348078947604797 - 0.481714513 52339645 -0.29247251388486895 0.11681669066169853 0.16887305261775154 0.32495959501921945 -0.24682672853645826 - 4.7904414330872669E-3 -5.7900266747036966E-2 2.9111110242594052E-2 8.5806453919138548E-2 - 4.7904414330872669E-3 -0.1236181336257971 0.22956488874110192 -0.10462072050828475 - 0.17341315386888034 0.10143177182222018 - 1.3448504176201581E-2 -9.4941538427384842E-2 0.25446764576613212 7.0374647750391262E-2 0.14689414589897609 9.3649631380165133E-2
- 44. 8.5806453919138548E-2 -2.2182184144957873E-2 2.9111110242594052E-2 0.17361554913703792 8.5646776191079965E-2 -0.32442214427952187 0.10915381791613044 0.98462255527003073 - 0.29247251388486895 -1.3448504176201581E-2- 0.17341315386888034 -6.7032750310309197E-2- 0.2475211260226029 -0.12034958707188714 0.33032153816060372 0.23574694229753668 0.10143177182222018 0.18438858886780096 0.25225466155680287 0.31052356968077888 0.25889920427547253 0.29786517280885505 2.9111110242594052E-2 4.5640412193803215E-2 - 6.7032750310309197E-2 0.29896443150542851 0.1917412582497775 3.7409913057288691E-2 4.5640412193803215E-2 0.56138108014580013 1.9916107498190172E-2 -4.0315427874716647E-2 0.11681669066169853 -5.9523849754508973E-2 8.1477314581293925E-2 -0.10442206356054484 0.19926035023767774 0.27563332902225035 - 4.190170657921044E-2 0.21485850767108694 LOGprice2 Residuals REG_2_LOG_MULTSUMMARY OUTPUTRegression StatisticsMultiple R0.7467922543R Square0.5576986711Adjusted R Square0.5290309924Standard Error0.2276384021Observations116ANOVAdfSSMSFSignifican ce FRegression77.05661100741.008087286819.45391799921.2429 3168712197E- 16Residual1085.59647814780.0518192421Total11512.6530891 551CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept5.23597090890.73843125387.09066806430.0000 0000013.77227205846.69966975943.77227205846.6996697594 LOGprice10.32907186280.11279107022.91753471480.0042928 0770.10550039430.55264333130.10550039430.5526433313LO
- 45. Gprice2-1.56858375870.2535926771- 6.18544579820.0000000113-2.0712484387-1.0659190787- 2.0712484387- 1.0659190787LOGprice30.04917479520.11948387090.4115601 1080.6814774825-0.18766296550.2860125558- 0.18766296550.2860125558LOGprice4- 0.0311828290.1347088638-0.23148312650.8173774992- 0.29819916670.2358335088-0.29819916670.2358335088Bad p- values highlighted in red for 2nd partLOGprice5- 0.11255832050.1009833933-1.11462208590.2674866635- 0.3127249250.087608284-0.3127249250.087608284Bad coefficient signs in red, good in greenfeat20.30347740940.0727931914.16903566450.000061927 90.15918867650.44776614240.15918867650.4477661424disp20 .02178741310.06792740630.32074554750.7490230314- 0.11285649040.1564313167- 0.11285649040.1564313167Prediction Model:X'sLN if required:RESIDUAL OUTPUTIntercept11LOGprice10.0453214084- 3.0939757655ObservationPredicted LOGsales2ResidualsLOGprice20.055- 2.900422093719.03752307330.0041619327LOGprice30.035965 532- 3.325194243329.00706050090.0231958093LOGprice40.035440 4248-3.339902165739.0115388388- 0.0527414927LOGprice50.0267793642- 3.620123679748.9985773232- 0.0153885239feat21159.0070605009- 0.1119789691disp21169.0337558921-0.1519195871Predicted LOG Sales29.440757245979.0182883837- 0.0350995844Predicted Sales212591.247968778989.0309841217- 0.149147816799.33454641340.0338228228109.05988090490.18 65985136119.0200990874-0.1516858027129.0127319212- 0.2436911104139.0006580951- 0.1188217901149.03572912060.192058165159.0238005653-
- 47. 0.0941797755819.9014373098-0.1780338191829.5025785642- 0.2938394736839.20088436650.2309982754849.66176204230.6 45589337859.1984720763- 0.1682157661869.1466864430.1625938769879.151819495- 0.0025038248889.15348949320.1022065805899.2580953566- 0.1828876586909.2817189618- 0.007840569919.72951857090.0659382375929.44319706430.22 1017555939.27789974990.146260846949.95489084920.073289 1473959.28426641770.2907170679969.28082048570.35243190 8979.30826219430.2733658339989.29741001370.32318398319 99.235634790.11620514421009.18221706650.18615216971019. 14649286160.10920321211029.37549085760.34791263311039. 36264657670.22558213561049.30429440870.05584432841059. 28627402850.08209520771069.90263630450.29650149021079. 32457895120.03555978581089.6628989022- 0.30276016521099.28604272790.15350278671109.2531084123- 0.03479987061119.21435996270.06848711129.107830141- 0.01088245641139.134060010.26657008841149.17895885530.2 9804422191159.1840344515- 0.02456640991169.59638146240.2562337598 LOGprice1 Residual Plot -2.861419935946397 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.9679955185823914 -2.9679955185823914 - 2.9679955185823914 -2.861419935946397 - 2.9679955185823914 -2.9679955185823914 - 2.9032670458818974 -2.9032670458818974 - 2.9032670458818974 -2.9032670458818974 - 2.861419935946397 -3.0636096959570769 - 2.9380531619673129 -2.9380531619673129 - 3.2875897174162527 -3.2875897174162527 - 3.2072252076482255 -2.9380531619673129 - 2.9380531619673129 -3.2466003728830555 -
- 48. 2.9380531619673129 -2.9380531619673129 - 2.9380531619673129 -2.9380531619673129 - 2.9380531619673129 -2.9380531619673129 - 3.6341545544246898 -2.9380531619673129 - 3.470748444623271 -2.9380531619673129 - 3.470748444623271 -3.470748444623271 - 2.9380531619673129 -3.3303312657935233 - 2.9380531619673129 -2.9380531619673129 - 3.470748444623271 -3.470748444623271 - 2.9380531619673129 -2.9380531619673129 - 3.470748444623271 -3.470748444623271 - 3.470748444623271 -2.9380531619673129 - 3.3749815395312623 -3.3749815395312623 - 2.9380531619673129 -2.9380531619673129 - 2.9380531619673129 -3.470748444623271 - 2.9380531619673129 -3.1539147156897847 - 2.9937697106410548 -3.0703211305450635 - 3.0804735020090814 -3.0804735020090814 - 3.0804735020090814 -3.0804735020090814 - 3.0736938150237028 -3.0636096959570769 - 3.470748444623271 -3.1564428273844145 - 3.0636096959570769 -3.0636096959570769 - 3.1493313015690938 -3.2875897174162527 - 3.3059304182461906 -3.3303312657935233 - 3.2466003728830555 -3.0636096959570769 - 3.0636096959570769 -3.0636096959570769 - 3.470748444623271 -3.0636096959570769 - 3.470748444623271 -3.5766674635070084 - 3.5766674635070084 -3.5094867622605435 - 3.1489643887243015 -3.2425923514855168 - 3.1896903884355008 -3.0636096959570769 - 3.2013951485679368 -3.3524072174927233 - 3.0636096959570769 -3.0636096959570769 - 3.0636096959570769 -3.0636096959570769 - 3.2466003728830555 -3.3639111143047891 - 3.470748444623271 -3.0636096959570769 -
- 49. 3.1693418897459242 -3.0636096959570769 - 3.0636096959570769 -3.0636096959570769 - 3.075056767204976 -3.097626581235331 - 3.0636096959570769 -3.1328414875263975 - 3.1480803350743174 -3.2466003728830555 - 3.1902895439159105 -3.0437414927403514 - 3.1328414875263975 -3.0639249531370387 4.1619326844042348E-3 2.3195809263739875E-2 - 5.2741492654689637E-2 -1.5388523936998411E-2- 0.11197896910451277 -0.1519195870635528 - 3.50995844476234E-2 -0.14914781666833221 3.3822822809407072E-2 0.18659851364921032 - 0.15168580273356724 -0.24369111038771507 - 0.11882179005493754 0.19205816501026618 - 0.30056929042626557 6.2051746545261 466E-2 8.4903324245836131E-2 -0.29689926491954566 - 6.4720708960823714E-2 -4.581337095343585E-3 0.23251803035969587 -0.22177153523416848 - 4.7590340465099246E-2 0.58375831809160239 0.20564023009232102 7.8755963432517007E-2 0.51090698090992248 -0.21565742915167085 - 0.59800993265573865 -0.24711999599430712 - 9.8209704566624367E-2 -3.8990250953823136E-3- 5.5355504595869576E-2 -5.7658674355479889E-2- 4.375948909108196E-2 -0.178521711300764 - 0.19075497458069535 -0.40586574782538953 - 0.38559358255629306 -0.53311529755166731 - 0.42592904216059857 -0.29239764953607583 - 8.6497752886350199E-2 -0.35240226484524229 0.11415270411667677 -0.35849188370524487 - 0.15818782293305489 -0.18452942419721019 - 0.21652533911472283 1.680797429818881E-2 - 0.27537865761999392 0.15951116759276296 0.12590719938752137 -0.16342150660754662 - 0.42536415507265168 -0.22835882432011978 8.9927975303165653E-2 0.12449942049117801
- 50. 7.520258121907375E-3 -8.7462204467250615E-2- 3.5289428326276351E-2 5.1975353746005482E-3 3.6064692811349275E-2 0.10023555303772191 1.1911670346099967E-2 -0.10453971658825978 - 3.8653703379468496E-2 -8.980750447451058E-2 - 0.13331369770756929 0.13867183061905131 0.1626582868489912 -4.5002036422078007E-2 0.30154294191599718 0.11106628813392838 0.21182396355530209 0.20705805331344607 0.21079143055112581 0.11632943894721137 0.14665369287286367 -9.4179775507331698E-2- 0.17803381911985916 -0.29383947362691742 0.23099827542137774 0.64558933698992327 - 0.16821576614753475 0.16259387686853444 - 2.5038248442665889E-3 0.10220658045315467 - 0.18288765864943457 -7.8405690316003529E-3 6.5938237470939853E-2 0.2210175550128799 0.1462608459502270 4 7.3289147308987168E-2 0.29071706791229701 0.35243190802640534 0.27336583393334735 0.32318398313523744 0.11620514421322881 0.18615216968668058 0.10920321212555706 0.34791263305617015 0.22558213564022722 5.584432840769793E-2 8.2095207710736418E-2 0.29650149017881589 3.5559785833669366E-2 -0.30276016515534643 0.15350278672502249 -3.4799870642736153E-2 6.848710003845504E-2 -1.0882456426855924E-2 0.26657008836983032 0.2980442219098336 - 2.4566409930200095E-2 0.25623375980823759 LOGprice1 Residuals LOGprice2 Residual Plot -2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 -
- 51. 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8082158998829323 -2.8082158998829323 - 2.8175614591851099 -2.8193220342207224 -3.040468 1673953826 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.861419935946397 -2.861419935946397 - 2.9370703615340887 -3.1805569605660642 - 3.1805569605660642 -2.9370703615340887 - 2.9370703615340887 -3.2861959898590136 - 3.2861959898590136 -3.2861959898590136 - 2.9370703615340887 -2.9370703615340887 - 2.9370703615340887 -2.9370703615340887 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.1080614585279105 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.1805569605660642 -3.1805569605660642 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.0624954904263681 - 3.0624954904263681 -3.006203572747602 - 3.2319821715291743 -3.2319821715291743 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.085018963651827 -3.085018963651827 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.0220747437612556 - 3.0404681673953826 -3.0270067782516215 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.1805569605660642 -3.1285726135817997 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.006203572747602 - 3.006203572747602 -3.1805569605660642 -
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- 54. 0.15350278672502249 -3.4799870642736153E-2 6.848710003845504E-2 -1.0882456426855924E-2 0.26657008836983032 0.2980442219098336 - 2.4566409930200095E-2 0.25623375980823759 LOGprice2 Residuals LOGprice3 Residual Plot -3.5223062542881207 -3.115079031186557 - 3.115079031186557 -3.2875897174162527 - 3.115079031186557 -3.115079031186557 - 3.2875897174162527 -3.2875897174162527 - 3.470748444623271 -3.115079031186557 - 3.115079031186557 -3.115079031186557 - 3.2875897174162527 -3.2875897174162527 - 3.115079031186557 -3.115079031186557 - 3.115079031186557 -3.470748444623271 - 3.115079031186557 -3.470748444623271 - 3.115079031186557 -3.3303312657935233 - 3.19742614784067 -3.2587217334154004 - 3.470748444623271 -3.6341545544246898 - 3.6341545544246898 -3.6341545544246898 - 3.2587217334154004 -3.2587217334154004 - 3.7601069634023041 -3.7601069634023041 - 3.2587217334154004 -3.2587217334154004 - 3.5766674635070084 -3.2587217334154004 - 3.2587217334154004 -3.3044677552036044 - 3.3044677552036044 -3.3044677552036044 - 3.3044677552036044 -3.3044677552036044 - 3.2587217334154004 -3.2587217334154004 - 3.3044677552036044 -3.3044677552036044 - 3.3044677552036044 -3.5935692743096115 - 3.3044677552036044 -3.3044677552036044 - 3.3044677552036044 -3.3044677552036044 - 3.3044677552036044 -3.470748444623271 - 3.3044677552036044 -3.3044677552036044 - 3.3044677552036044 -3.3044677552036044 -
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