the objetive to create linear regression follow the below template to answer the questions Part 1: Introduction State the relationship to be explored. This should include a thorough discussion of the two variables and how they are likely to be related. Explain for the layman the intuitive relationship between the two variables.

Part 2: The Data Give an overview of the data. For small data sets the data can be put in a table, for large data sets the data should have a descriptive table with mean, range, defining variable names for use in regression etc. If you use variable names this is the place to define themthey can be defined in the table of summary statistics if that suits you. Notice that in the next section you will have to produce the regression equation and it will look much clearer if you dont have variables which are several words or very long strings of letters.

Part 3: Regression Results You should first describe the regression you are going to perform. The results of the regression should be shown in equation form. Also show the regression table output from Excel in your document (i.e. you cannot use a trend line to get the resultant equation). You should then interpret the regression results. This includes describing precisely what the coefficients on the independent variable(s) is (are) and explain how the changes in these variables are related to changes in the dependent variable. Include also a short discussion on the intercept term and its interpretation. Lastly, you should note if the results seem reasonable if possible.

Part 4: The sufficient conditions for good estimates Linear in parameters Here you should show scatter plots of the independent variable(s) versus the dependent variable and comment if it looks like there is a linear relationship between the two. If there is no linear relationship between the variables then note this as a weakness of the analysis. Random sampling In this section you are to discuss how the data was collected and if there is any reason to believe that the data is not representative of the population you are studying. Variation in the independent variable(s) Discuss if there are any gaps in the independent variables that might make it difficult to extrapolate from the data to an estimate. Is there enough variation in the independent variables in relation to the dependent variable? (It might be good to refer to the data summary (part 2) or the scatter plots created above.) Zero mean of the error term conditional on the independent variable(s) For this the obvious thing to think about is everything else that can affect the dependent variable that you have not included as an independent variable in your regression and opine on if these things are likely to be correlated with any of your independent variables which are included in the regression analysis.

You should now know that the data you want is not the revenue per square foot for your stores. What you would like to have is data on revenue and data on square foot for each store. You fire your old intern and hire a new one that can anticipate what you need before you ask for it and here is the new data. Use the data in the two charts below and the analysis model you just learned to estimate the revenue earned for each square foot of store space. This is the only graded deliverable in this module. See the grading rubric and the example analysis as a guide to do well. Good luck. Revenue $40,000,000 $37,813,050 $35,000,000 $34,422,312 $32,124,443 $32,859,907 $30,000,000 $26,904,195 $25,000,000 $20,334,222 $18,826,126 $20,296,634 $20,000,000 $15,306,386 $15,000,000 $11,987,067 $10,000,000 $10,566,338 $5,654,277 $5,000,000 $0 Loc1 Loc2 Loc3 Loc4 Loc5 Loc6 Loc7 Loc8 Loc9 Loc 10 Loc11 Loc12 Table 1: Revenue collected from individual Big Grocery stores, by location Square foot of each location 120,000 100,000 97,392 92,389 93,222 83,615 78,342 80,000 71,347 69,071 60,000 50,367 50,167 SqFt 40,000 31,891 20,000 13,601 4,296 Loc5 Loch Loc8 Loc9 Loc10 Loc11 Loc12 Loc1 Loc2 Loc3 Loch Loc7 Table 2: Big Grocery store square footage, by location You should now know that the data you want is not the revenue per square foot for your stores. What you would like to have is data on revenue and data on square foot for each store. You fire your old intern and hire a new one that can anticipate what you need before you ask for it and here is the new data. Use the data in the two charts below and the analysis model you just learned to estimate the revenue earned for each square foot of store space. This is the only graded deliverable in this module. See the grading rubric and the example analysis as a guide to do well. Good luck. Revenue $40,000,000 $37,813,050 $35,000,000 $34,422,312 $32,124,443 $32,859,907 $30,000,000 $26,904,195 $25,000,000 $20,334,222 $18,826,126 $20,296,634 $20,000,000 $15,306,386 $15,000,000 $11,987,067 $10,000,000 $10,566,338 $5,654,277 $5,000,000 $0 Loc1 Loc2 Loc3 Loc4 Loc5 Loc6 Loc7 Loc8 Loc9 Loc 10 Loc11 Loc12 Table 1: Revenue collected from individual Big Grocery stores, by location Square foot of each location 120,000 100,000 97,392 92,389 93,222 83,615 78,342 80,000 71,347 69,071 60,000 50,367 50,167 SqFt 40,000 31,891 20,000 13,601 4,296 Loc5 Loch Loc8 Loc9 Loc10 Loc11 Loc12 Loc1 Loc2 Loc3 Loch Loc7 Table 2: Big Grocery store square footage, by location