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Semelhante a 7 1 And 7 2 Ratio, Proportion
BUSI 620 Questions for Critical Thinking 3 Salvatore’s Chapter 6: a. Discussion Questions: 1, 7, and 15. b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257). Note: 1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236). 2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period. 3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts. Salvatore’s Chapter 7: a. Discussion Questions: 3, 11, and 12. b. Problems: 4, 12, and 13. Note: 1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC. MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40. 2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale. 3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns. 4. P13(a): See figure (7-4) on page 276. DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...
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See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10 22 0.956 23 30 80 7 1 4.7 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: 0 = male, 1 = female 31 24 1.043 23 29 60 4 1 3.9 0 F A Midpoint – salary grade midpoint Raise – percent of last raise 35 24 1.043 23 23 90 4 1 5.3 1 F A Grade – job/pay grade Degree (0= BS\BA 1 = MS) 36 23 1.000 23 27 75 3 1 4.3 1 F A Gender1 (Male or Female) Compa - salary divided by midpoint 37 22 0.956 23 22 95 2 1 6.2 1 F A 42 24 1.043 23 32 100 8 1 5.7 0 F A 3 34 1.096 31 30 75 5 1 3.6 0 F B 18 36 1.161 31 31 80 11 1 5.6 1 F B 20 34 1.096 31 44 70 16 1 4.8 1 F B 39 35 1.129 31 27 90 6 1 5.5 1 F B 7 41 1.025 40 32 100 8 1 5.7 0 F C 13 42 1.050 40 30 100 2 1 4.7 1 F C 22 57 1.187 48 48 65 6 1 3.8 0 F D 24 50 1.041 48 30 75 9 1 3.8 1 F D 45 55 1.145 48 36 95 8 1 5.2 0 F D 17 69 1.210 57 27 55 3 1 3 0 F E 48 65 1.140 57 34 90 11 1 5.3 1 F E 28 75 1.119 67 44 95 9 1 4.4 1 F F 43 77 1.149 67 42 95 20 1 5.5 1 F F 19 24 1.043 23 32 85 1 0 4.6 1 M A 25 24 1.043 23 41 70 4 0 4 0 M A 40 25 1.086 23 24 90 2 0 6.3 0 M A 2 27 0.870 31 52 80 7 0 3.9 0 M B 32 28 0.903 31 25 95 4 0 5.6 0 M B 34 28 0.903 31 26 80 2 0 4.9 1 M B 16 47 1.175 40 44 90 4 0 5.7 0 M C 27 40 1.000 40 35 80 7 0 3.9 1 M C 41 43 1.075 40 25 80 5 0 4.3 0 M C 5 47 0.979 48 36 90 16 0 5.7 1 M D 30 49 1.020 48 45 90 18 0 4.3 0 M D 1 58 1.017 57 34 85 8 0 5.7 0 M E 4 66 1.157 57 42 100 16 0 5.5 1 M E 12 60 1.052 57 52 95 22 0 4.5 0 M E 33 64 1.122 57 35 90 9 0 5.5 1 M E 38 56 0.982 57 45 95 11 0 4.5 0 M E 44 60 1.052 57 45 90 16 0 5.2 1 M E 46 65 1.140 57 39 75 20 0 3.9 1 M E 47 62 1.087 57 37 95 5 0 5.5 1 M E 49 60 1.052 57 41 95 21 0 6.6 0 M E 50 66 1.157 57 38 80 12 0 4.6 0 M E 6 76 1.134 67 36 70 12 0 4.5 1 M F 9 77 1.149 67 49 100 10 0 4 1 M F 21 76 1.134 67 43 95 13 0 6.3 1 M F 29 72 1.074 67 52 95 5 0 5.4 0 M F Score: Week 1. Measurement and Description - chapters 1 and 2 .
See comments at the right of the data set..docx
See comments at the right of the data set..docx
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DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
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Question 3) Perform a regression analysis using compa as the dependent variable and the same independent variables as used in question 2. (Question #2 found below the table). Show the result, and interpret your findings by answering the same questions. Note: be sure to include the appropriate hypothesis statements. A ) Regression hypotheses Ho: Ha: Coefficient hyhpotheses (one to stand for all the separate variables) Ho: Ha: B )Interpretation: For the Regression as a whole: What is the value of the F statistic: What is the p-value associated with this value: Is the p-value < 0.05? Do you reject or not reject the null hypothesis: What does this decision mean for our equal pay question (Do M and F get paid equaly?): C )For each of the coefficients: Intercept, Midpoint, Age, Perf. rating, Service, Gender, Degree What is the coefficient\'s p-value for each of the variables: Is the p-value < 0.05? Do you reject or not reject each null hypothesis: What are the coefficients for the significant variables? Using only the significant variables, what is the equation? Compa = Is gender a significant factor in compa: If so, who gets paid more with all other things being equal? How do we know? ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Gr 1 66.1 1.159 57 34 85 8 0 5.7 0 M E 2 25.9 0.834 31 52 80 7 0 3.9 0 M B 3 35.2 1.135 31 30 75 5 1 3.6 1 F B 4 55.3 0.971 57 42 100 16 0 5.5 1 M E 5 49.6 1.033 48 36 90 16 0 5.7 1 M D 6 78.3 1.168 67 36 70 12 0 4.5 1 M F 7 42.3 1.058 40 32 100 8 1 5.7 1 F C 8 22.8 0.990 23 32 90 9 1 5.8 1 F A 9 78 1.164 67 49 100 10 0 4 1 M F 10 23.3 1.014 23 30 80 7 1 4.7 1 F A 11 23.6 1.025 23 41 100 19 1 4.8 1 F A 12 60.8 1.067 57 52 95 22 0 4.5 0 M E 13 40.6 1.014 40 30 100 2 1 4.7 0 F C 14 21.7 0.943 23 32 90 12 1 6 1 F A 15 21.8 0.949 23 32 80 8 1 4.9 1 F A 16 37.4 0.934 40 44 90 4 0 5.7 0 M C 17 57 1.000 57 27 55 3 1 3 1 F E 18 33.5 1.081 31 31 80 11 1 5.6 0 F B 19 23 1.000 23 32 85 1 0 4.6 1 M A 20 36 1.162 31 44 70 16 1 4.8 0 F B 21 76 1.135 67 43 95 13 0 6.3 1 M F 22 43.7 0.911 48 48 65 6 1 3.8 1 F D 23 25.3 1.098 23 36 65 6 1 3.3 0 F A 24 48.9 1.019 48 30 75 9 1 3.8 0 F D 25 25.8 1.122 23 41 70 4 0 4 0 M A 26 23.3 1.013 23 22 95 2 1 6.2 0 F A 27 42.3 1.057 40 35 80 7 0 3.9 1 M C 28 75.2 1.122 67 44 95 9 1 4.4 0 F F 29 80.9 1.208 67 52 95 5 0 5.4 0 M F 30 49 1.020 48 45 90 18 0 4.3 0 M D 31 24.2 1.054 23 29 60 4 1 3.9 1 F A 32 27.5 0.886 31 25 95 4 0 5.6 0 M B 33 63.6 1.115 57 35 90 9 0 5.5 1 M E 34 28.6 0.922 31 26 80 2 0 4.9 1 M B 35 22.4 0.976 23 23 90 4 1 5.3 0 F A 36 23.6 1.026 23 27 75 3 1 4.3 0 F A 37 24.3 1.057 23 22 95 2 1 6.2 0 F A 38 63 1.105 57 45 95 11 0 4.5 0 M E 39 34.8 1.123 31 27 90 6 1 5.5 0 F B 40 24.3 1.057 23 24 90 2 0 6.3 0 M A 41 42.8 1.071 40 25 80 5 0 4.3 0 M C 42 23 0.998 23 32 100 8 1 5.7 1 F A 43 75.4 1.125 67 42 95 20 1 5.5 0 F F 44 60.7 1.065 57 45 90 16 0 5.2 1 M E 45 57.9 1.206 48 36 95 8 1 5.2 1 F D 46 62.2 1.091 57 39 75 20 0 3.9 1 M E 47 62.2 1.091 57 37 .
Question 3)Perform a regression analysis using compa as the depend.pdf
Question 3)Perform a regression analysis using compa as the depend.pdf
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Ashford 4: - Week 3 - Discussion 1 Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated. ANOVA In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario. Ashford 4: - Week 3 - Discussion 2 Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated. Effect Size Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data? Ashford 4: - Week 3 - Assignment Problem Set Week Three Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations. All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet. Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment. See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10 22 0.956 23 30 80 7 1 4.7 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: ...
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
fredharris32
Question 1) Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.) What variables can be used in a Pearson\'s Correlation table (which is what Excel produces)? b )Place table here (C8): c )Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are significantly related to Salary? To compa? d )Looking at the above correlations - both significant or not - are there any surprises -by that I mean any relationships you expected to be meaningful and are not and vice-versa? e )Does this help us answer our equal pay for equal work question? ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Gr 1 66.1 1.159 57 34 85 8 0 5.7 0 M E 2 25.9 0.834 31 52 80 7 0 3.9 0 M B 3 35.2 1.135 31 30 75 5 1 3.6 1 F B 4 55.3 0.971 57 42 100 16 0 5.5 1 M E 5 49.6 1.033 48 36 90 16 0 5.7 1 M D 6 78.3 1.168 67 36 70 12 0 4.5 1 M F 7 42.3 1.058 40 32 100 8 1 5.7 1 F C 8 22.8 0.990 23 32 90 9 1 5.8 1 F A 9 78 1.164 67 49 100 10 0 4 1 M F 10 23.3 1.014 23 30 80 7 1 4.7 1 F A 11 23.6 1.025 23 41 100 19 1 4.8 1 F A 12 60.8 1.067 57 52 95 22 0 4.5 0 M E 13 40.6 1.014 40 30 100 2 1 4.7 0 F C 14 21.7 0.943 23 32 90 12 1 6 1 F A 15 21.8 0.949 23 32 80 8 1 4.9 1 F A 16 37.4 0.934 40 44 90 4 0 5.7 0 M C 17 57 1.000 57 27 55 3 1 3 1 F E 18 33.5 1.081 31 31 80 11 1 5.6 0 F B 19 23 1.000 23 32 85 1 0 4.6 1 M A 20 36 1.162 31 44 70 16 1 4.8 0 F B 21 76 1.135 67 43 95 13 0 6.3 1 M F 22 43.7 0.911 48 48 65 6 1 3.8 1 F D 23 25.3 1.098 23 36 65 6 1 3.3 0 F A 24 48.9 1.019 48 30 75 9 1 3.8 0 F D 25 25.8 1.122 23 41 70 4 0 4 0 M A 26 23.3 1.013 23 22 95 2 1 6.2 0 F A 27 42.3 1.057 40 35 80 7 0 3.9 1 M C 28 75.2 1.122 67 44 95 9 1 4.4 0 F F 29 80.9 1.208 67 52 95 5 0 5.4 0 M F 30 49 1.020 48 45 90 18 0 4.3 0 M D 31 24.2 1.054 23 29 60 4 1 3.9 1 F A 32 27.5 0.886 31 25 95 4 0 5.6 0 M B 33 63.6 1.115 57 35 90 9 0 5.5 1 M E 34 28.6 0.922 31 26 80 2 0 4.9 1 M B 35 22.4 0.976 23 23 90 4 1 5.3 0 F A 36 23.6 1.026 23 27 75 3 1 4.3 0 F A 37 24.3 1.057 23 22 95 2 1 6.2 0 F A 38 63 1.105 57 45 95 11 0 4.5 0 M E 39 34.8 1.123 31 27 90 6 1 5.5 0 F B 40 24.3 1.057 23 24 90 2 0 6.3 0 M A 41 42.8 1.071 40 25 80 5 0 4.3 0 M C 42 23 0.998 23 32 100 8 1 5.7 1 F A 43 75.4 1.125 67 42 95 20 1 5.5 0 F F 44 60.7 1.065 57 45 90 16 0 5.2 1 M E 45 57.9 1.206 48 36 95 8 1 5.2 1 F D 46 62.2 1.091 57 39 75 20 0 3.9 1 M E 47 62.2 1.091 57 37 95 5 0 5.5 1 M E 48 70.1 1.230 57 34 90 11 1 5.3 1 F E 49 61.7 1.083 57 41 95 21 0 6.6 0 M E 50 61.4 1.077 57 38 80 12 0 4.6 0 M E ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Gr 1 66.1 1.159 57 34 85 8 0 5.7 0 M E 2 25.9 0.834 31 52 80 7 0 3.9 0 M B 3 35.2 1.135 31 30 75 5 1 3.6 1 F B 4 55.3 0.971 57 42 100 16 0 5.5 1 M E 5 49.6 1.033 48 36 90 16 0 5.7 1 M D 6 78.3 1.168 67 36 70 12 0 4.5 1 M F 7 42.3 1.058 40 32 100 8 1 5.7 1 F C 8 22.8 0.990 23 32 90 9 1 5.8 1 F A 9 78 1.164 67 .
Question 1)Create a correlation table for the variables in our dat.pdf
Question 1)Create a correlation table for the variables in our dat.pdf
ARCHANASTOREKOTA
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wk_1Score:Week 1.Measurement and Description - chapters 1 and 2<1 point>1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatioGenderIDRaiseServiceGender 1GradeMidpointSalaryDegreeAgePerformance RatingCompab.For each variable that you did not call ratio, why did you make that decision?Nominals (Gender, Gender1, Degree) classification data that reflects differences in kind, discrete variables, qualitative.Ordinals (Grade, ID) classification is order, reflects differences in degree, i.e., rankings, letter grades, stages in developmentIntervals (Raise, Compa) classification is order and equal intervals, measurable differences in amount, i.e., IQ scores, degrees of temperature, estimations, ratings<1 point>2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.SalaryCompaAgePerf. Rat.ServiceOverallMean45Standard Deviation19.2014Range55FemaleMean38Standard Deviation18.294Range55MaleMean52Standard Deviation17.776Range53<1 point>3What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?20%b. Randomly selected male being in grade E? 40%Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?A. is analyzing entire population (Males & Females) and the B. is examining male population.<1 point>4For each group (overall, females, and males) find:OverallFemaleMalea.The value that cuts off the top 1/3 salary in each group.55k50k60kHint: can use these Fx functionsb.The z score for each value:0.520800.65595646540.45004Excel's standize functionc.The normal curve probability of exceeding this score:1-normsdist functiond.What is the empirical probability of being at or exceeding this salary value?e.The value that cuts off the top 1/3 compa in each group.f.The z score for each value:g.The normal curve probability of exceeding this score:h.What is the empirical probability of being at or exceeding this compa value?i.How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question?<2 points>5. What conclusio.
wk_1ScoreWeek 1.Measurement and Description - chapters 1 and 21 .docx
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Solve Eq Notes 01
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Order Op Example 01
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Translate And Fraction Example 01
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Solve Eq Notes 02
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Solve Eq Example 01
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Chapter 3 Quiz 1 Review
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Review Chapter 2
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1 5 Postulates And Theorems Relating Points, Lines Filled In
1 5 Postulates And Theorems Relating Points, Lines Filled In
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3 1 Parallel Lines And Planes Filled Out
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Similarity 7-1 Ratio and Proportion 7-2 Properties of Proportions
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Properties of Proportions
Means Extremes
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Baixar agora