QNT/561 - Inferential Statistics: NCC Sales and Marketing Analysis

This assignment analyzes the relationship between sales and marketing spending at NCC using inferential statistics. The student investigates a research question: "Will spending more funding for promotions lead to better sales for NCC?" Mock sales and marketing spend data is provided for 36 months. The assignment utilizes the Pearson product-moment correlation coefficient to measure the linear correlation between sales and marketing spending. The null hypothesis assumes no relationship. The student calculates the correlation coefficient (r = 0.9674815801) and p-value. The p-value is less than 0.0001, leading to the rejection of the null hypothesis. The analysis concludes that there is a strong positive correlation between marketing spending and sales, and thus, increased marketing spending supports sales. The assignment includes references to statistical resources used for the analysis.

Running head: INFERENTIAL STATISTICS FOR NCC WK 5
Descriptive Statistics for NCC WK 4
Jared Norrell
QNT\561
25th July 2016
Professor Villalobos

Running head: INFERENTIAL STATISTICS FOR NCC WK 5
Table of Content
Research Question
Mock data for the independent and dependent variables
Appropriate statistical tool to test the hypothesis
Hypothesis
References

Running head: INFERENTIAL STATISTICS FOR NCC WK 5
Research Question
Sales have declined the past three quarters at NCC and because of the continued decline in sales
the NCC marketing team is asked to find innovative ways to increase sales. Sales as a dependent
variable can be looked at in many ways, such as sales of a specific car model, sales of a category
like SUV or sport cars, overall sales of a particular car. Independent variables are product,
promotion and price. Consumers want safe automobiles that are environmentally friendly,
dependable and safe. The research question for this sales team deals with how to sale
environmentally friendly, dependable and safe automobiles in a declining market. This team’s
question is “Will spending more funding for promotions lead to better sales for NCC?”
Mock data for the independent and dependent variables
Below is the data for the research question. Sales data is the number of units sold and marketing
spend is the amount spent on promotion etc in US dollars.
Months Sales (units) Marketing Spend ($)
Month 1 8400 98000
Month 2 8369 96000
Month 3 8350 95000
Month 4 8317 91000
Month 5 8300 98000
Month 6 8280 95000
Month 7 8290 94000
Month 8 8300 95000
Month 9 8265 95000
Month 10 8270 96000

Running head: INFERENTIAL STATISTICS FOR NCC WK 5
Month 11 8260 93000
Month 12 8250 93000
Month 13 8243 91000
Month 14 8310 94000
Month 15 8210 88000
Month 16 8190 89000
Month 17 8170 88000
Month 18 8150 87000
Month 19 8145 87000
Month 20 8160 88000
Month 21 8190 88000
Month 22 8139 85000
Month 23 8135 86000
Month 24 8100 84000
Month 25 8089 84000
Month 26 8081 84000
Month 27 8070 80000
Month 28 8063 80000
Month 29 8050 79000
Month 30 8041 79000
Month 31 8037 78000
Month 32 8010 75000
Month 33 8000 74000
Month 34 7993 74000
Month 35 7991 75000
Month 36 7975 72000
Appropriate statistical tool to test the hypothesis
Based on the given data, the statistical tool that will be used in correlation. The Pearson product-
moment correlation coefficient, i.e. r will measure the linear correlation between the sales units
and the marketing spending. There are two three possibilities for the correlation variable. If the
value is +1, it means a total positive correlation. 0 stands for no correlation and -1 stands for
negative correlation. Correlation is used to establish the linear dependence between the two

Running head: INFERENTIAL STATISTICS FOR NCC WK 5
variables. In our case, it will be used to establish the linear dependence between sales and market
spending.
Hypothesis
There are two variables
u1 = Sales in units
u2 = Marketing spending
From the null hypothesis, we assume that there is no relationship between the two variables and
marketing spending will not impact the overall sales.
H0 = u1 - u2 = 0 ( It is the null hypothesis that states that marketing spending won’t affect the
overall sales)
From the correlation calculation the value of variable r, is 0.9674815801.
Using the r and sample size of 36 at confidence level 95%, the p value will be calculated.
If p < 0.05 implies hypothesis will be rejected ("Steps in Hypothesis Testing (1 of 5)", 2016)
If p > 0.05 implies null hypothesis hold true.
For the given sample and r value, p < 0.0001 implies null hypothesis is rejected ("Quick P Value
from Pearson (R) Score Calculator", 2016)
This value shows that there is a strong correlation between the two variables and both are
linearly dependent. Higher the marketing spending implies higher sales.
Interpretation

Running head: INFERENTIAL STATISTICS FOR NCC WK 5
From the hypothesis and calculation of r and p value it is evident that our assumption is
incorrect. The variation in the marketing spending will affect the sales figure with a confidence
level of 95%. Even from the linear data correlation analysis, it is clear that increase in the value
of marketing spending has a positive correlation with the sales. So our research question can be
best answered that the spending on the marketing budget will support sales.
References
Quick P Value from Pearson (R) Score Calculator. (2016). Socscistatistics.com. Retrieved 25
July 2016, from http://www.socscistatistics.com/pvalues/pearsondistribution.aspx
Steps in Hypothesis Testing (1 of 5). (2016). Davidmlane.com. Retrieved 25 July 2016, from
http://davidmlane.com/hyperstat/B35642.html