I need a report on the folleoing data please (Not Calculation i done calculation

Question

I need a report on the folleoing data please (Not Calculation i done calculation already just write report please )

You are a consultant who works for the Diligent Consulting Group. Your client, the New Star Grocery Company, believes that there may be a relationship between the number of customers who visit the store during any given month (“customer traffic”) and the total sales for that same month. In other words, the greater the customer traffic, the greater the sales for that month. To test this theory, the client has collected customer traffic data over the past 12-month period, and monthly sales for that same 12-month period (Year 1).

Length requirements: 4–5 pages minimum (not including Cover and Reference pages). NOTE: You must submit 4–5 pages of written discussion and analysis. This means that you should avoid use of tables and charts as “space fillers.”

Provide a brief introduction to/background of the problem.

Your written (in Word) analysis should discuss the logic and rationale used to develop the LR equation and chart.

Provide complete, meaningful, and accurate recommendation(s) concerning how the New Star Grocery Company might use the LR equation to forecast future sales. (For example, how reliable is the LR equation in predicting future sales?) What other recommendations do you have for the client?

New Star Grocery Company Insert chart here Year 1 Customers Sales ($000) Number Month Customers (x) Sales       (y) XY X2 Y2 January 185 230 1 January 185 230        42,550.0        34,225        52,900.00 February 241 301 2 February 241 301        72,541.0        58,081        90,601.00 March 374 310 3 March 374 310      115,940.0      139,876        96,100.00 April 421 389 4 April 421 389      163,769.0      177,241      151,321.00 May 425 421 5 May 425 421      178,925.0      180,625      177,241.00 June 259 300 6 June 259 300        77,700.0        67,081        90,000.00 July 298 318 7 July 298 318        94,764.0        88,804      101,124.00 August 321 298 8 August 321 298        95,658.0      103,041        88,804.00 September 215 202 9 September 215 202        43,430.0        46,225        40,804.00 October 282 265 10 October 282 265        74,730.0        79,524        70,225.00 November 235 312 11 November 235 312        73,320.0        55,225        97,344.00 December 300 298 12 December 300 298        89,400.0        90,000        88,804.00 Totals 3556 3644 1,122,727.0 1,119,948 1,145,268.00 Mean 296 303.67 X-bar Y-bar b1 0.6480 b0        111.65

Explanation / Answer

Here dependent variable (Y) = sales

And independent variable (X) = customers

Here we have to fit regression and find scatter plot and analyze and interpret the data.

This we can done in MINTAB.

steps :

ENTER data into MINITAB sheet --> STAT --> Regression --> Regression --> Response : select sales column --> Predictors : select customers column --> Results : select second options --> ok --> ok

————— 7/8/2017 10:54:42 AM ————————————————————

Welcome to Minitab, press F1 for help.

Regression Analysis: Sales ($000) versus Customers

The regression equation is
Sales ($000) = 112 + 0.648 Customers

Predictor Coef SE Coef T P
Constant 111.65 39.23 2.85 0.017
Customers 0.6480 0.1284 5.05 0.001

S = 33.0385 R-Sq = 71.8% R-Sq(adj) = 69.0%

Analysis of Variance

Source DF SS MS F P
Regression 1 27791 27791 25.46 0.001
Residual Error 10 10915 1092
Total 11 38707

Here intercept (b0) = 112

slope (b1) = 0.648

Interpretation of slope :

For one unit change in customers will be 0.648 unit increase in sales.

Overall significance and individual significance:

We can test the same hypothesis using overall significance and individual significance.

Here we can test the hypothesis that,

H0 : B = 0 Vs H1 : B not= 0

where B is population slope for customers.

Assume alpha = level of significance = 0.05

Here for overall significance test statistic follows F-distribution and for individual significance test statistic follows t-distribution.

F = 25.46

P-value = 0.001

T = 5.05

P-value = 0.001

Here P-value < alpha

Reject H0 at 0.05 level of significance.

COnclusion : The population slope for customers is differ than 0.

OR customers is significant variable.

R-sq = 71.8%

Interpretation : It expresses the proportion of variation in sales which is explained by variation in customers.

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