Use the dataset named “ACS2020 CS allstates.xls” to conduct hypothesis tests and determine confidence intervals in JMP between different variables
(a) Examine the distribution for the variables “female”, “male”, and “female male ratio” and report their summary statistics
(b) Click on the red triangle next to the variable and produce a 95% confidence interval for each variable.
(c) Formally test this hypothesis by using a test for difference using “mean test”. Make sure to test the female variable mean against the mean for men and vice versa, while the mean for female male ratio, should be compared to 1.0. (d) Conduct these tests again but using “Test Equivalence” with the belief that a difference of 1% or 5% difference from the hypothesized mean is practically zero. (e) Select “Tables” > “Stack” under the menu to stack the variables female and male. Name this variable ”female male” and Label it ”Stacked” and then perform the following actions. i. Select Fit Y by X under Analyze and declare your newly created variable “female male” to be Y, Response and “Stacked” to be X, Factor. ii. Select “Means/Anova/Pooled t” and then ”t Test” iii. Select “Normal Quantile Plot” and then “Plot Actual by Quantile” (f) Redo (e i.) - (e iii.) but for the for a new variable ”Ln female male” where this is the natural log of “female male” (g) Go back to “ACS2020 CS allstates.xls” and Select “Analyze” > “Specialized Modeling” > “Matched Pairs” and assign the variables “female” and “male” to Y, Paired Response. (h) Describe the evidence (tests of difference [equal and non-equal variance], equivalence test, ANOVA, and a matched) concerning whether the population of women and men is the same within the United States. Make sure to identify what you believe is the best test and why. Make sure to compare the results obtained using untransformed data and logged data and the results obtained using “female male ratio” with that obtained using “female” and “male” variables separately and when those variables are matched. 1 II. Use IPEDS data (should should be your all three data sets merged: Characteristics, TuitionFees, and StudentAid) to explore mean differences for many categories within a single variable (a) Create a new categorical variables based upon carnegie. This new variable should be called “carnegiecombined”. The entries for this variable should be either Associates Colleges; Baccalaureate Colleges; Masters Colleges and Universities; Doctoral/Research Universities; or Other. (b) Select “Analyze” > “Fit Y by X” and assign “Published in-state tuition and fees 2021- 22” to Y and “carnegiecombined” to X, Factor and then use the red triangle to select “Means/Anova”. (c) After clicking on the red triangle select “Compare Means” > “Each Pair, Student’s t” to conduct a Oneway Analysis by the new combined Carnegie classification type. (d) Adjust for the likelihood of Type I error (due to making many comparisons) by selecting “Compare Means” > “All Pairs, Tukey HSD” (e) Examine to see if the variances of each group are equal by selecting “Unequal Variances” (f) Describe the evidence concerning whether the colleges and universities across the different combined Carnegie classifications have similar sticker prices for their 2020-2021 in state tuition. Which type charge the most, which say they cost the least? Are these differences significant? Explain. III. Use the combined IPEDS data from II. to explore multivariate relationships and transform “Instate average tuition for full-time undergraduates” and “Ave amount of total awards (federal, state, local or institutional aid)” into natural logs by creating two new variables: LN Tuition, LN AID (a) Explore relationship between LN AID and LN Tuition by selecting “Analyze” > “Fit Y by X” and assigning LN AID to Y,
Response, LN Tuition to X, Factor. (b) Select Summary Statistics under the red triangle. What does this tell you about the correlation between LN AID and LN Tuition? (c) Select Fit Line under the red triangle. What is the regression equation? If there is a 1-unit change in tuition, by how much does AID change? Is this relationship significantly different from zero? (d) Right click next to the parameter estimates and select “Columns” > “Lower 95%” and do this again but for “Upper 95%” (e) Examine the residuals by clicking “Plot Residuals” after selecting the red triangle next to “linear fit”.
Is there evidence that your residuals are normally distributed? After examining the residuals to you think this model is a good fit? (f) Describe what you have learned about the relationship between the financial aid offered by an academic institution and its [posted tuition. Do you think that posted tuition determines the aid package or is it the other way around (Aid determines tuition)? What would happen if you switched the variables? Do you get the same results? Basically I just need screenshots of all the steps. Due today 4/21 11:59 EST
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