In this paper we will consider basic statistical concepts and hypothesis testing. The first step is to calculate mean, median and standard deviation of all variables in the given data sample. This means that we have to calculate descriptive statistics – measures of central tendency and measures of variance.
Descriptive statistics and graphs are able to present data in the most convenient form for analysis.
Descriptive statistics allows summarizing the initial results obtained by observation or experiment. All calculations are reduced to descriptive statistics grouping data from their values, their frequency distribution of construction, identification of central tendency and distribution, finally, to the assessment of the spread of data in relation to the central tendency found.
/> Presentation of descriptive statistics is usually the first step in any analysis. The purpose of presenting data in the form of descriptive statistics - to draw conclusions and make strategic (for analysis) solutions based on available data.
All calculations of the descriptive statistics were completed in MS Excel and presented in .xls file.
The next step is to show how analysis of variance may be applied to this case. Which hypotheses we can test with ANOVA regarding to the given data set? Assume, we want to investigate, if there any differences in average Ferris State Score by games in 2012-2013, 2013-2014 and 2014-2015? We have to compare three sets of Ferris State Score.
Null hypothesis: There is no significant difference between these scores
Alternative Hypothesis: Not all mean values are equal (at least one mean is significantly different from others)
H0: μ1=μ2=μ3Ha:not all means are equal
Set level of significance of the test alpha = 0.05
According to the results, we obtain the critical value of the test F=3.31583 which is not significant at 5% level of significance because p-value is 0.077228 and it is higher than 0.05.
Hence, we have failed to reject the null hypothesis. There is no evidence to say about any differences (at 5% level of significance).