Unless otherwise specified, round all your answers to three decimal places. you will use the utilities in JMP, called Fit Model and Graph Builder. 1. The act of reflection is a critical tool for learning. If we don't know where we've been, what we've accomplished (or not accomplished), and what we want to aspire to, then we cannot learn from our mistakes and successes. Keeping this in mind, go back and look at your lab 3 submissions. Look over any incorrect submissions. In the space below explain why your answers are wrong or incomplete. If you didn't miss any points on lab 3, write down one or two different parts you struggled with from lab 3 or thought was more difficult. Describe what specifically was difficult about these problems? The purpose here is to provide feedback for yourself that you can use to reference as you solve additional lab problems. Additionally, use this reflection to help you avoid making similar mistake on future assignments and reference these reflections when you study for your exams. 2. Women Apparel. Assume that you are interested in opening a women apparel franchise. Before you con- sider investing in such a franchise we would like to have some idea of the likely revenue we should expect from such a venture. One possible approach is to determine how two variables influence sales at stores in a chain of similar women apparel franchises (annually, in dollars per square foot of retail space). The dataset mall sales.JMP contains information on a random sample of 65 different stores. One possible explanatory variable is the median household income of the area served by the mall (in thousands of dollars); another explanatory variable is the number of competing women apparel stores in the same mall. We expect outlets in malls that serve more affluent neighborhoods will have higher sales, whereas we expect outlets that face more competition will have lower sales. Use JMP when necessary. We will use this example to create 3 different mod- els (labeled Model 1, Model 2 and Model 3). Answer each questions based on the model indicated for each part. (a) (Model 1) Use Fit Model to estimate a simple linear regression model with sales per square foot as the response. Use the explanatory variable median household income ($1000). Upload the simple linear regression output. (b) (Model 1) Write out the estimated simple linear regression model for this example. (c) (Model 1) Using the model report the predicted sales per square foot for a women apparel franchise in a community with a median Income of . (Every student in the class may have a different value for the underlined quantity in Canvas.) Keep at least 3 decimals for all parts of your calculation. (d) (Model 1) For this simple linear regression model provide an interpretation of the R2using the context of the example. (e) (Model 1) For this simple linear regression model provide an interpretation of the estimated slope. (f) (Model 1) For the population linear regression model provide an interpretation of the population slope. (g) (Model 1) Make a 90% interval for the Sales per square foot of a women apparel franchise in a community with median Income of $68,000 (h) (Model 1) Make a 90% interval for the mean Sales per square foot of all women apparel franchises in communities with median Income of $68,000. (i) (Model 1) Determine which plots are appropriate to use in order to evaluate each of the model assumptions. (j) (Model 1) Make a 90% confidence interval for the slope associated with Median income ($1000). (k) (Model 2) Estimate a simple linear regression model with sales per square foot as the response. Use the explanatory variable number of competitors. Upload the simple linear regression output. (l) (Model 2) Based on the output for Model 2, perform a hypothesis test to determine if there is statistical evidence for a negative linear relationship between Sales per square foot and competitors.

Here is the JMP File just copied and pasted. Course Hero won't let me upload a JMP file. Please please please help.

Sales ($/sq. ft)	Income ($000)	Competitors
464	60	1
552	61	1
377	52	1
674	67	1
560	63	1
523	61	1
546	60	1
569	61	0
579	58	0
502	56	1
615	62	0
551	52	1
400	54	1
434	58	1
534	54	0
500	51	0
475	58	0
521	71	0
490	60	3
455	72	2
486	61	3
551	77	3
577	69	3
514	70	3
509	48	2
609	71	2
531	57	3
359	55	3
503	59	3
521	58	2
554	74	2
475	54	3
473	51	2
834	86	2
459	63	3
346	59	2
709	83	3
310	54	2
477	77	3
587	76	3
430	54	3
504	53	2
559	62	3
464	64	2
457	50	2
475	63	2
534	66	3
377	61	3
400	62	2
609	86	4
611	83	4
504	61	4
458	59	5
304	52	5
640	90	5
485	66	4
538	83	4
437	62	4
767	91	5
358	64	5
486	70	5
404	54	4
562	69	4
487	68	4
839	105	6