excel master series-Anova in-excel-to-improve-marketing

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How To Use All 3 Types of ANOVA Built Into Excel To Make Your Internet Marketing More Effective http:// blog.excelmasterseries.com /

What Is ANOVA? ● ANOVA stands for “Analysis of Variance” ● ANOVA tells whether varying an element effected the output ● Marketers use ANOVA to find out whether changing an element of a marketing campaign had an effect http:// blog.excelmasterseries.com /

The 3 Types of ANOVA Built Into Excel 1) Single-Factor ANOVA 2) Two-Factor ANOVA Without Replication 3) Two-Factor ANOVA With Replication http:// blog.excelmasterseries.com /

Single-Factor ANOVA Tests Only 1 Input Element ● Single-Factor ANOVA tests the Null Hypothesis for that factor, which states that varying the factor does not affect the outcome ● An underlying assumption for all ANOVA tests is that no other factors vary except the ones being tested http:// blog.excelmasterseries.com /

Single-Factor ANOVA Test Data Ready for Input to Excel ● Yellow highlighted cells are the input range for Excel http:// blog.excelmasterseries.com /

Single-Factor ANOVA Excel Output http:// blog.excelmasterseries.com /

Two-Factor ANOVA Without Replication Tests 2 Input Elements ● Two-Factor ANOVA Without Replication tests the Null Hypothesis for each factor, which states that varying the factor does not affect the outcome http:// blog.excelmasterseries.com /

Two-Factor ANOVA Without Replication Test Data Ready for Input to Excel ● Yellow highlighted cells are the input range for Excel http:// blog.excelmasterseries.com /

Two-Factor ANOVA Without Replication Excel Output http:// blog.excelmasterseries.com /

Two-Factor ANOVA With Replication Tests 2 Input Elements and the Interaction Between the 2 Elements ● Two-Factor ANOVA With Replication replicates the Two-Factor Test in 2 places in order to test the interaction between the 2 factors ● Two-Factor ANOVA With Replication tests the Null Hypothesis for each factor, which states that varying the factor does not affect the outcome. The interaction has its own Null Hypothesis which is tested which states that Interaction has no effect.

Two-Factor ANOVA With Replication Test Data Ready for Input to Excel ● Yellow highlighted cells are the input range for Excel http:// blog.excelmasterseries.com /

Two-Factor ANOVA With Replication Excel Output http:// blog.excelmasterseries.com /

Summary of ANOVA That Was Run 1) Create 5 sets of Ad Text for 1 Ad Group 2) Create 3 sets of Headlines for that Ad Group 3) Run all 15 Ad Text / Headline Combinations 4) Prepare test results for input into Excel ANOVA 5) Run all 3 types of ANOVA on the test results 6) Evaluate the output for each Excel ANOVA http:// blog.excelmasterseries.com /

Interpretation of Single-Factor ANOVA Excel Output ● Headlines P Value (0.0013) less than Alpha (0.05) we are 95% certain that varying Headline affects output http:// blog.excelmasterseries.com /

Interpretation of Two-Factor ANOVA Without Replication Excel Output ● Headlines P Value (0.0005) less than Alpha (0.05) we are 95% certain that varying Headline affects output ● Headlines P Value (0.627) greater than Alpha (0.05) we are not 95% certain that varying Headline affects output http:// blog.excelmasterseries.com /

Interpretation of Two-Factor ANOVA With Replication Excel Output ● Headlines P Value (1.18 E06) less than Alpha (0.05) we are 95% certain that varying Headline affects output ● Ad Text P Value (0.1568) greater than Alpha (0.05) we are not 95% certain that varying Ad Text affects output ● Interaction P Value (0.0402) less than Alpha (0.05) we are 95% certain that varying Interaction affects output http:// blog.excelmasterseries.com /

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How do I do a T test, correlation and ANOVA in Spss? Solution One-way between-subjects ANOVA A one-way between-subjects ANOVA allows you to determine if there is a relationship between a categorical independent variable (IV) and a continuous dependent variable (DV), where each subject is only in one level of the IV. To determine whether there is a relationship between the IV and the DV, a one-way between- subjects ANOVA tests whether the means of all of the groups are the same. If there are any differences among the means, we know that the value of the DV depends on the value of the IV. The IV in an ANOVA is referred to as a factor, and the different groups composing the IV are referred to as the levels of the factor. A one-way ANOVA is also sometimes called a single factor ANOVA. A one-way ANOVA with two groups is analogous to an independent-samples t- test. The pvalues of the two tests will be the same, and the F statistic from the ANOVA will be equal to the square of the t statistic from the t-test. To perform a one-way between-subjects ANOVA in SPSS • Choose Analyze General Linear Model Univariate. • Move the DV to the Dependent Variable box. • Move the IV to the Fixed Factor(s) box. • Click the OK button. The output from this analysis will contain the following sections. • Between-Subjects Factors. Lists how many subjects are in each level of your factor. • Tests of Between-Subjects Effects. The row next to the name of your factor reports a test of whether there is a significant relationship between your IV and the DV. A significant F statistic means that at least two group means are different from each other, indicating the presence of a relationship. You can ask SPSS to provide you with the means within each level of your between-subjects factor by clicking the Options button in the variable selection window and moving your withinsubjects variable to the Display Means For box. This will add a section to your output titled Estimated Marginal Means containing a table with a row for each level of your factor. The values within each row provide the mean, standard error of the mean, and the boundaries for a 95% confidence interval around the mean for observations within that cell. Post-hoc analyses for one-way between-subjects ANOVA. A significant F statistic tells you that at least two of your means are different from each other, but does not tell you where the differences may lie. Researchers commonly perform post-hoc analyses following a significant ANOVA to help them understand the nature of the relationship between the IV and the DV. The most commonly reported post-hoc tests are (in order from most to least liberal): LSD (Least Significant Difference test), SNK (Student- Newman-Keuls), Tukey, and Bonferroni. The more liberal a test is, the more likely it will find a significant difference between your means, but the more likely it is that this difference is actually just due to chance. 14 Although it is the most liberal, simulations ha.

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Chapter 18 – Pricing Setting in the Business World There are few Methods for setting pricing – costs methods vs demand methods Formulas considering costs and mark up will help you to do the Problem set assignment: 1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100) Formula for setting price with the markup method SP = Cost/(1- Markup %) Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be…. SP = 15/(1-.40)=.60 = $25.00 2. Understand Role of different costs – fixed, variable, total costs and average costs 3. What is the breakeven point? Formula for calculating the Break Even point. BE = Total Fixed Cost/Fixed cost contribution Fixed Cost Contribution=Price – variable cost 4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price. .ANOVA Analysis of Variance is a method of testing the equality of three or more population means by analyzing sample variance. One-Way ANOVA The one-way ANOVA is used to compare three or more population means when there is one factor of interest. Requirements  The populations have distributions that are approximately normal.  The populations have the same variance.  The samples are simple random samples of quantitative data.  The samples are independent of each other.  The different samples are from populations that are categorized in only one. way One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test. Example A professor at a local University believes there is a relationship between head size and the major of the students in her biostatistics classes. She takes a random sample of 20 students from each of three classes and records their major and head circumference. The data are shown in the following table. Step 1: State the null hypothesis. Mean 1 equals mean 2 equals mean 3 equals mean 4. Step 2: State the Alternative hypothesis. At least one mean is different. Step 3: State the Level of Significance. The level of significance is 0.05. Step 4: State the test statistic. variance between samples variance within samples F  The test statistic follows the F distribution which has two degrees of freedom, one for the numerator and one for the denominator. The calculations for the test statistic are complicated, so a software program is generally used for the calculations. We will be using Microsoft Excel for this example. Step 5: Calculate The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the data into the spread sheet as shown here. Click on data and the data analysis tookpak button is on the right. When you click on the button a dialogue box appears. Choose ANOVA One Factor. Then another dialogue box appears. Input range is where the data is in the table. Be sure to put a check in the box for labels in.

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Your Paper was well written, however; I need you to follow the following Analysis Guidance for Intervention Data. I will give you a passing grade when you submit with these by the 26th of April at 1pm EST This document is designed to provide a summary of the key steps for analysing intervention data. The main analysis is conducted using the general linear model function in SPSS. This document does not cover how to clean data for analysis. (Data for the PARS module has already been cleaned so students do not have to undertake this part of the analysis.) This document is written with the PARS assignment in mind, so please refer to statistical texts for details on how to check assumptions, and a broader overview of how to interpret the output of intervention analyses in SPSS. Preparing Scales When using scales, ensure you compute scale reliabilities (Cronbachs Alpha using the function Analyse>Scale>Reliability analysis). Make sure scales are recoded as required by the specific scale you’re using. If you find poor reliability, that might indicate scale items have not been coded as required (e.g. a scale item may need reverse coding). If scale reliability is poor, then you may want to exclude it from the analysis, remove a low-loading item, or report why you think the reliability is poor and justify why you decided to include it. Scale items should be aggregated or averaged using the compute variable function in SPSS (Transform>Compute variable) for the main analysis, as directed by the scale authors. (For the PARS assignment, scale reliability statistics can be reported in the appendix.) Calculating Means and Standard Deviations It is useful at this stage to calculate the means and standard deviations for the data using the function Analyse>Descriptive Statistics. For intervention data comparing more than one condition, you need to isolate a condition in the dataset before generating the means and standard deviations for that condition. The analyses testing the effect of an intervention with individuals in different conditions (i.e. between-subject) are essentially testing whether there is a significant difference in the means of groups in different conditions. The means for the different conditions show whether levels are increasing or decreasing, and this is useful for interpreting the results of the analysis. Isolate study conditions using the function Data>Select cases, and use the function ‘If condition satisfied’. In the PARS data, use cohort as the variable in the rule (i.e. ‘Cohort = 1’ for the intervention group, or ‘Cohort = 2’ for the control group). When you have either of these rules applied, SPSS will only run the analysis on the cases selected by that rule. For example, if the rule applied is ‘Cohort = 1’ only cases with the value 1 in the cohort variable will be included in the analysis. Bivariate Correlations As part the analysis, you need to run bivariate correlations. Use the function Analyse>Correlate>Bivariate. (For ...

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1 Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.) a. Interpret the results. What variables seem to be important in seeing if we pay males and females equally for equal work? 2 Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Mid, age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of expressing an employee’s salary, we do not want to have both used in the same regression.) Ho: The regression equation is not significant. Ha: The regression equation is significant. Ho: The regression coefficient for each variable is not significant Ha: The regression coefficient for each variable is significant Sal The analysis used Sal as the y (dependent variable) and SUMMARY OUTPUT mid, age, ees, sr, g, raise, and deg as the dependent variables (entered as a range). Regression Statistics Multiple R 0.99215498 R Square 0.9843715 Adjusted R Square 0.98176675 Standard Error 2.59277631 Observations 50 ANOVA df SS MS F Significance F Regression 7 17783.7 2540.52 377.914 8.44043E-36 Residual 42 282.345 6.72249 Total 49 18066 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -4.009 3.775 -1.062 0.294 -11.627 3.609 -11.627 3.609 Mid 1.220 0.030 40.674 0.000 1.159 1.280 1.159 1.280 Age 0.029 0.067 0.439 0.663 -0.105 0.164 -0.105 0.164 EES -0.096 0.047 -2.020 0.050 -0.191 0.000 -0.191 0.000 SR -0.074 0.084 -0.876 0.386 -0.244 0.096 -0.244 0.096 G 2.552 0.847 3.012 0.004 0.842 4.261 0.842 4.261 Raise 0.834 0.643 1.299 0.201 -0.462 2.131 -0.462 2.131 Deg 1.002 0.744 1.347 0.185 -0.500 2.504 -0.500 2.504 Interpretation: Do you reject or not reject the regression null hypothesis? Do you reject or not reject the null hypothesis for each variable? What is the regression equation, using only significant variables if any exist? What does result tell us about equal pay for equal work for males and females? 3 Perform a regression analysis using compa as the dependent variable and the same independent variables as used in question 2. Show the result, and interpret your findings by answering the same questions. Note: be sure to include the appropriate hypothesis statements. 4 Based on all of your results to date, is gender a factor in the pay practices of this company? Why or why not? Which is the best variable to use in analyzing pay practices - salary or compa? Why? .

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BUS 308 Week 3 Lecture 3 Setting up ANOVA and Chi Square Expected Outcomes After reading this lecture, the student should know how to: 1. Set-up the data for an ANOVA analysis. 2. Set-up and perform an ANOVA test. 3. Set-up a table of mean differences. 4. Set-up and perform a Chi Square test. Overview Setting up the ANOVA test is quite similar to how the t and F tests were set up. The Chi Square set-up is a bit more complex, as it is not found in the Data Analysis list of tools. ANOVA The set-up of ANOVA within Excel is very similar to how we set up the F and T tests last week; place the data set in appropriate groups and then use the ANOVA input box. One difference this week is that the Fx (or Formulas) list does not include an option for ANOVA, so we need to use the Data | Analysis tools. Data Set-up Single Factor. As with the t-test, ANOVA has a couple of versions to select between. Each is used to answer slightly different questions, and these will be examined below. The most significant difference lies in the data table used for each version. We will be working primarily with the ANOAV Single Factor, which deals with examining possible differences between the means of a single variable within different groups. A question of whether or not the mean compa-ratios are equal across the grades is an example of the kind of question answered with this approach. Question 1. Week 3’s first question is about salary mean equality across the grades. Our lecture example will deal with compa-ratio mean equality across the grades. The set-up for the Single Factor ANOVA we just went through assumed this. The initial steps in the hypothesis testing process are similar to what we have done before: Step 1: Ho: All Compa-Ratio means are equal across the grades Ha: At least one compa-ratio mean differs Notice that these are the standard ANOVA – Single factor null and alternate hypothesis statements that identify the specific variable (compa-ratio) and statistic (mean) that we are testing, and merely say “no difference” and “at least one differs.” Step 2: Alpha = 0.05 Step 3: F statistic and Single Factor ANOVA; used to test multiple means Step 4: Decision Rule: Reject Ho if the p-value < 0.05 Step 5: Conduct the test – place the test function in cell K08. As with the F and T tests, we need to group the data into distinct groups. For example, if we are going to test the compa-ratio mean across grades, then the data must be set-up in a table with grades across the top, as in the screen shot below. Note that as was done with the T and F test input data, the raw or initial data was listed and then sorted. Values were then copied into related groups; we used male and female groups for the F and t tests and grade groups for this test. Test Set-up. Go to the Data | Analysis and select ANOVA Single Factor gives us the following input screen. This is completed for our compa-ratio ...

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U3 IP.sav MKTG420_U3IP.doc Unit 3 Individual Project 1 MACROBUTTON DoFieldClick Type your Name Here American Intercontinental University MACROBUTTON DoFieldClick Type your Paper Title Project Type: MKTG420 Unit 3 Individual Project MACROBUTTON DoFieldClick Date of Submission Abstract This is a single paragraph, no indentation is required. The next page will be an abstract; “a brief, comprehensive summary of the contents of the article; it allows the readers to survey the contents of an article quickly” (Publication Manual, 2010). The length of this abstract should be 35-50 words (2-3 sentences). NOTE: the abstract must be on page 2 and the body of the paper will begin on page 3. MACROBUTTON DoFieldClick Type your Paper Title Introduction Remember to always indent the first line of a paragraph (use the tab key). The introduction should be short (2-3 sentences). The margins, font size, spacing, and font type (italics or plain) are set in APA format. While you may change the names of the headings and subheadings, do not change the font. Part 1: Research background on the scales Introduce the concept and be sure to indent the first line of the paragraph. Provide background on each of the 4 scales (assurance, empathy, reliability and responsiveness), not limited to a simple definition but as a measurement that aids marketers. Discuss how the questions in the survey are transformed into "scales" (also called "factors"). In other studies using SERVQUAL, how many and what types of respondents were included? Part 1 of the Individual Project should be 1 page in length. Be sure to cite your resources. Part 1: Concept of Scales/Factors Introduce the concept and be sure to indent the first line of the paragraph. Part 1: SERVQUAL Samples Introduce the concept and be sure to indent the first line of the paragraph. Part 2: (Full-Text Research) Service Quality and Segmentation Introduce the concept and be sure to indent the first line of the paragraph. Connect information from at least 3 articles. Do not write and overview or critique of the articles. Synthesize and connect the information contained to develop a solid understanding of how service quality and segmentation are related. Part 2 of the Individual Project should be 2 pages in length and should be predominately from at least three articles in AIU's full-text databases. Be sure to cite your resources. Part 3: Null/Hypo 1, ANOVA, Decision Attached is a small set of data that has been collected from brand loyal customers of Store 1 and Store 2. Write out a Null hypothesis and an alternate hypothesis for each of the 4 aspects of service quality that are include in the analysis (assurance, empathy, reliability and responsiveness) to see if there is a difference between stores. Run 4 ANOVAs to test the Null hypotheses. State the decision for each of the tests. Part 3: Null/Hypo 2, ANOVA, Decision Write out a Null hypothesis and an alternate hypothesis ...

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excel master series-Anova in-excel-to-improve-marketing

1. How To Use All 3 Types of ANOVA Built Into Excel To Make Your Internet Marketing More Effective http:// blog.excelmasterseries.com /

2. What Is ANOVA? ● ANOVA stands for “Analysis of Variance” ● ANOVA tells whether varying an element effected the output ● Marketers use ANOVA to find out whether changing an element of a marketing campaign had an effect http:// blog.excelmasterseries.com /

3. The 3 Types of ANOVA Built Into Excel 1) Single-Factor ANOVA 2) Two-Factor ANOVA Without Replication 3) Two-Factor ANOVA With Replication http:// blog.excelmasterseries.com /

4. Single-Factor ANOVA Tests Only 1 Input Element ● Single-Factor ANOVA tests the Null Hypothesis for that factor, which states that varying the factor does not affect the outcome ● An underlying assumption for all ANOVA tests is that no other factors vary except the ones being tested http:// blog.excelmasterseries.com /

5. Single-Factor ANOVA Test Data Ready for Input to Excel ● Yellow highlighted cells are the input range for Excel http:// blog.excelmasterseries.com /

6. Single-Factor ANOVA Excel Output http:// blog.excelmasterseries.com /

7. Two-Factor ANOVA Without Replication Tests 2 Input Elements ● Two-Factor ANOVA Without Replication tests the Null Hypothesis for each factor, which states that varying the factor does not affect the outcome http:// blog.excelmasterseries.com /

8. Two-Factor ANOVA Without Replication Test Data Ready for Input to Excel ● Yellow highlighted cells are the input range for Excel http:// blog.excelmasterseries.com /

9. Two-Factor ANOVA Without Replication Excel Output http:// blog.excelmasterseries.com /

10. Two-Factor ANOVA With Replication Tests 2 Input Elements and the Interaction Between the 2 Elements ● Two-Factor ANOVA With Replication replicates the Two-Factor Test in 2 places in order to test the interaction between the 2 factors ● Two-Factor ANOVA With Replication tests the Null Hypothesis for each factor, which states that varying the factor does not affect the outcome. The interaction has its own Null Hypothesis which is tested which states that Interaction has no effect.

11. Two-Factor ANOVA With Replication Test Data Ready for Input to Excel ● Yellow highlighted cells are the input range for Excel http:// blog.excelmasterseries.com /

12. Two-Factor ANOVA With Replication Excel Output http:// blog.excelmasterseries.com /

13. Summary of ANOVA That Was Run 1) Create 5 sets of Ad Text for 1 Ad Group 2) Create 3 sets of Headlines for that Ad Group 3) Run all 15 Ad Text / Headline Combinations 4) Prepare test results for input into Excel ANOVA 5) Run all 3 types of ANOVA on the test results 6) Evaluate the output for each Excel ANOVA http:// blog.excelmasterseries.com /

14. Single-Factor ANOVA Excel Output http:// blog.excelmasterseries.com /

15. Interpretation of Single-Factor ANOVA Excel Output ● Headlines P Value (0.0013) less than Alpha (0.05) we are 95% certain that varying Headline affects output http:// blog.excelmasterseries.com /

16. Two-Factor ANOVA Without Replication Excel Output http:// blog.excelmasterseries.com /

17. Interpretation of Two-Factor ANOVA Without Replication Excel Output ● Headlines P Value (0.0005) less than Alpha (0.05) we are 95% certain that varying Headline affects output ● Headlines P Value (0.627) greater than Alpha (0.05) we are not 95% certain that varying Headline affects output http:// blog.excelmasterseries.com /

18. Two-Factor ANOVA With Replication Excel Output http:// blog.excelmasterseries.com /

19. Interpretation of Two-Factor ANOVA With Replication Excel Output ● Headlines P Value (1.18 E06) less than Alpha (0.05) we are 95% certain that varying Headline affects output ● Ad Text P Value (0.1568) greater than Alpha (0.05) we are not 95% certain that varying Ad Text affects output ● Interaction P Value (0.0402) less than Alpha (0.05) we are 95% certain that varying Interaction affects output http:// blog.excelmasterseries.com /

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