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Performing and Interpreting Simple Regression and the Results

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One of the assertions is that changes in Federal Reserve policy can affect inflation. Using the data on the growth rate of M2 and inflation in your spreadsheet, run a regression of the rate of inflation on the rate of growth of the money supply. Do the data support a connection between the rate of increase in the money supply and inflation? What does the coefficient on the money supply variable tell you? What is the meaning of the p-value and how is it used? Is the regression coefficient significant? Is faster money growth always associated with higher inflation? Explain your findings.

Please use the coaching slides #9, 10, & 11 from the attached Power Point slides to solve the above question. Data file is attached below as well. Please provide detailed verbal explanations to all as I do not remember anything about regression and I need to explain it all myself.

Thank you so much!

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Do the data support a connection between the rate of increase in the money supply and inflation?

No, the data does not support. We can first check the ANOVA table (from attached excel file). From the table, the p-value is 0.11 which is greater than significance level 0.05. Hence, the model that we build is not significant. Second, we can check the R square value from the excel output; the value is 0.33 which is very small. Only 33% ...

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The solution gives detailed steps on performing simple regression and then interpretating the results.

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Performing and Interpreting Results in a Simple Regression

Website
Your company maintains a corporate website, which is managed for it by an independent company ("Webmedia"). They suggest to you that you should pay to advertise your website on one of the most widely visited sites on the web, site W. As proof that this is worthwhile they give you numbers from another (anonymous) client, corporation X, showing the number of "hits" per week over a period of twenty weeks, during some of which they advertised their own site at site W. This data is contained in the dataset website.dta from the data archive. The column "hits" gives the number of hits per week, and the column "advatW" contains a 0 for weeks when the site was not advertised at W, and a 1 for weeks when it was.

Your first step is to regress the number of hits on the advertising dummy.

(a) Based on this regression, how many extra hits do you estimate you will get by advertising your site at site W?
(b) Give a 90% confidence interval for this number (of extra hits).
(c) Can you think of any reason you might not trust this confidence interval as a prediction of your extra hits? (i.e. why your actual confidence could be less than 90%?)
(d) Suppose now that your company has decided to value each hit at 1 cent. You are being offered a contract for an initial ten weeks of advertising space at $2,000 per week. Using your regression model, can you prove that buying the advertising would be profitable, at the significance level .05? Use the "klincom" command and answer with a formal hypothesis test.
(e) Would you in fact buy the advertising? Why might you give an answer here which is contrary to the answer in (d)?

hits advatW
1 331100 1
2 98457 0
3 247150 0
4 111540 1
5 371630 1
6 185060 1
7 515440 1
8 24040 0
9 280150 1
10 288230 0
11 217730 0
12 253800 0
13 238290 1
14 276270 1
15 71230 0
16 283670 1
17 37841 0
18 425950 1
19 101140 1
20 45186 1.

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