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27/08/2022
2: Copy/paste into Google Form the value in row 86 for column (variable) Friendliness in DATA1.
“`{r}
“`
#3: What is the mean of column (variable) Promotion in DATA1? Copy/paste into Google Form.
“`{r}
“`
#4: DATA1 contains data on customers to 3 different stores. Customers were asked
to rate Satisfaction with a store on a scale of Low, Medium, and High.
“`{r}
“`
Study the association between Satisfaction and Store. Copy/paste
into Google Form the estimated p-value (not the range) for the test of association
that is found after 500 permutation datasets. Add the argument seed=100 to associate.
#5: Let us predict Price (a relative measure of how satisfied people are with the prices at the store)
from Promotion (a relative measure of how satisfied people are with the promotions at the store)
in DATA1 using transformed variables.
Fit a simple linear regression model predicting Price from Promotion, then determine the best fitting set of transformations.
Copy/paste into Google Form the power to which we should raise x (Promotion).
Note: if the log transformation is suggested, enter 0.
“`{r}
“`
#6: Create a subset of DATA2 where q5 is strictly less than 50.12 .
Copy/paste into Google Form Spearman’s rank correlation between q5 and q6 on this subset.
Note: you may ignore all warnings output by R if they occur- they refer to ties in the data.
“`{r}
“`
#7: Find the variable that is most correlated with grade in DATA2, then fit a simple
linear regression predicting grade from that variable.
Copy/paste the RMSE of this model into Google Form
“`{r}
“`
#8: Using the regression model from #7, copy/paste into Google Form
the upper bound of a 94% prediction interval of the grade when the predictor is equal to 80.
Note: If it is asking for confidence interval then the confidence interval refers to the average of the grade.
For prediction interval, it refers to the particular value of the grade.
For guidance on creating the TO.PREDICT dataframe, refer to an in-class activity on the matter.
BAS 320-Business Analytics
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