Comp. Final Assignment - Shaurya

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Centennial College *

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MISC

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Marketing

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Jan 9, 2024

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Uploaded by AmbassadorFogSandpiper38

MKTG744003 Computer Applications for Marketing Analytics Individual Assignment ANOVA, Correlation and Regression Name: Shaurya Gupta Student ID: 301337846 Task 1: ANOVA Single factor analysis using Excel and SAS (5 pts) Data source: ANOVA Excel (given) The dataset encompasses the weekly revenue from three distinct sales categories: Delivery, Onsite, and Take-out. The manager is intent on ascertaining whether there exists a noteworthy disparity among these sales categories. Additionally, if such a difference is detected, the objective is to determine which of the three categories boasts the highest mean revenue. Formulate a tailored hypothesis based on the provided details. In this context, we are proceeding with the assumption that the mean revenue for each individual sales category is either equal, or any disparities between them amount to zero. Null Hypothesis (H0) : μ Revenue Delivery = μ Revenue Onsite = μ Revenue Take-out Alternative Hypothesis (H1) : μ Revenue Delivery ≠ μ Revenue Onsite ≠ μ Revenue Take-out, or at least one exhibits divergence. 2. Compute your work using Excel.

See Excel file for solution. SAS solution:

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3. Based on your results give a conclusion and write 1-3 sentences to describe your findings. Given the minuscule P Value (<0.001), which is notably less than the predetermined Level of Significance (0.05), the null hypothesis (H0) is unequivocally dismissed. The Onsite classification emerges as the unrivaled leader in average revenue when juxtaposed with its sales counterparts. Task 2: Covariance/Correlation using Excel and SAS (5Pts) 1. The data on Worksheet ‘Stock Data’ presents a given time stock report of Microsoft (NSFT), Tesla (TSLA), and Netflix (NFLX). (2Pts) a) Conduct Covariance analysis using Excel and SAS.

See Excel file for solution. SAS solution:

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b) Based on your results, if you were to invest in two stocks, which combination would be considered an excellent choice? If I had to pick stock pairs for investment, my choices would be Microsoft (MSFT) paired with Netflix (NFLX), or Tesla (TSLA) paired with Netflix (NFLX). The covariance between these stock pairs is negative. This implies that I can lower my risks – when one goes down, the other tends to go up, helping to balance potential losses. 2. Using the ‘Product Data’ Conduct correlation analysis on the following variables: (3pts) a) Income and Total Amt. See Excel file for solution. SAS solution:

b) Meat Products and Sweet Products. See Excel file for solution. SAS solution:

c) Discuss your results if those products have any correlations, and if so to what extent and direction. When we examine the relationship between "Income" and "Total Amount," we observe a robust and positive correlation between these two variables, with a correlation coefficient (r) of 0.82, which is very close to 1. Turning our attention to the comparison between "Meat Products" and "Sweet Products," we identify a moderate and positive correlation between these variables, as evidenced by a correlation coefficient (r) of 0.53. While not as close to 1 as the previous example, the correlation still indicates a meaningful connection between the two. Task 3: Regression analysis using Excel and SAS (5pts) Using the “Product data’ conduct regression analysis on Income and total Amt. in Excel

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and SAS. (Attach screenshots of tables and charts with your name as a footnote). 1. Create a hypothesis and give your conclusion, reject, or fail to reject Ho. See Excel file for solution. SAS solution: Null Hypothesis (H0): There isn't a statistically important connection between "Income" and "Total Amount." Alternative Hypothesis (H1): There's a statistically significant connection between "Income" and "Total Amount."

Our evidence supports rejecting the null hypothesis (H0). The P Value (<0.001), which is less than the significance threshold (0.05), reinforces this. With 95% confidence, we can affirm that a noteworthy relationship exists between "Income" and "Total Amount." 2. How significant was your result? What was the probability of being right with the conclusions? Considering the R² value of 0.68, we can assert that this model accurately predicts outcomes about 68% of the time, with a 95% level of confidence. 3. Create a scatterplot in Excel including the trend line regression equation. 4. Based on the equation estimate Y= Total Amount when X=Income. a) Find the average income of the respondents. See Excel file for solution.

SAS solution: The average income of the respondents is $52,926.53. b) If the average income were to increase by 10,000, based on the equation what would be the total amount spent on products? New average income = $52,926.53 + $10,000 = $62,926.53 Y = Total Amount X = Income Y = 0.0311X – 859.16 = 0.0311(62,926.53)-859.16 = $1,098.59 Based on the equation, the total amount spent on products will be $1,098.59.

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c) Using the standard error, what would be the highest and the lowest limits of your result? Standard Error = 120.082 Highest = $1,218.67 Lowest = $978.51

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