Create In No More Than 250 Words Inferential Statistics (hypothesis Test). Include: (a) The Research – Research Paper Example
Hypothesis Test Hypothesis Test Objective of the study is to investigate whether there was an increase in sales of Ford Motor Company cars with proper digital advertising. Study will use 100 randomly picked Ford Motor cars, advertised digitally and then advance sales.
Ho: There is no correlation in sales increase based on Ford Motor Company’s digital advertising.
H1: There is a correlation in sales increase based on Ford Motor Company digital advertising.
Because the confidence level on the alternative hypothesis is high, we use one-sided test hypothesis. One-sided hypothetical test easily detects desired effect compared to two-tailed test (Vittinghoff et al., 2012).
Research hypothesis are, Ho=190
From 100 test cars with known Population Standard, and Population Mean of 198, Sigma Level would be 15.
Test statistics (Z-Test) would be calculated as below,
Z= (198-190)/ (15/√100) = 5.33 (Harmon, 2011)
For accurate and efficient Hypothetical Test, the study uses a Significance Level of 0.05.
In statistical analysis, it is important to use confidence levels in estimation of measures in a population. Confidence level helps in making judgment about calculated test results when we do not know population mean. The Confidence Level of 95%, at significance level of 0.05, would reject the Null Hypothesis. Ford Motors should reject null hypothesis because test statistic probability is higher than the significance level. There is 95% confidence that sales increased through digital advertising. It is justified to reject the null hypothesis and accept H1 (Wilcox, 2012). Based on hypothesis test results, Ford’s digital advertising resulted in sales increase.
Harmon, M. (2011). Hypothesis Testing in Excel - The Excel Statistical Master. Florida: Mark Harmon.
Vittinghoff, E., Glidden, D. V., Shibosk, S. C., & McCulloch, C. E. (2012). Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York: Springer Science & Business Media.
Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing Statistical modeling and decision science. Waltham: Academic Press.