Introduction

Data analytics is a discipline of scrutinizing raw data to make inferences about an information. The methods and processes of data analytics have been mechanized into mechanical progressions and algorithms that work over raw data for human consumption. Some of its merits is that it help a business to improve its performance. The analysis of data is encompasses many miscellaneous types of analysis. Any type of information can be exposed to data analytics techniques to get some perception that can be utilized to improve stuffs. Analytics methods can expose trends and metrics that would else be lost in the bulk data. This data can then be used to advance processes to increase the general efficiency of a system or even business. This report will therefore describe constitutes outputs of the analysed data and show how it has been used to test various hypotheses.

Hypothesis Testing Statistical hypothesis testing, also called confirmatory data analysis is a prescribed technique for inspecting our thoughts about the world using statistics. It is usually used by researchers to test specific forecasts, called hypotheses that arise from theories. It is also mainly used to test whether experimental output have enough information to cast distrust on conventional wisdom. It helps scientist to determine whether the data from the sample is statistically significant.
San Jose State University Statistics Department, viewed hypothesis testing as one of the most important concepts in statistics since it enables us to determine if something really occurred, or if a certain treatment have positive influence, or if groups tend to differ from one another or if one variable foresees another. Confirmatory data analysis is considered to be one of the most vital practices for measuring the validity and reliability of results in any systematic exploration.
Example
In a certain city in the US it was considered at a certain time that people of certain color or race had lower intelligence capacity compared to the Hispanic’s. A hypothesis had to be performed which showed that intelligence is not based on color or race. People of various races, colors and cultures were given intelligence tests and the data was analyzed. Statistical hypothesis testing then proved that the results were statistically significant in that the similar measurements of intelligence between races are not merely sample error.
Hypothesis testing involves several steps which include;
i. State the null hypothesis (Ho) and alternate hypothesis (Ha or H1).
ii. Collect data in a way premeditated to test the hypothesis.
iii. Carry out an appropriate statistical test.
iv. Make conclusion whether to reject or fail to reject your null hypothesis.
v. Present your findings results and discussion section.

Testing differences

In statistics we have many circumstances where we may wish to compare means for two samples or even populations. The technique we use will entirely depend on the type of data we have and how it is grouped. However this comparison between two means of two samples have some significance which includes the following;
i. Evaluation of means tests helps us to determine if the experimental or control groups we are testing have similar means.
ii. It provides a way to test the hypothesis that the two control or experimental groups differ from each other.
Example
Is the night shift production less than the day shift production, are the rates of return from fixed asset investments different from those from common stock investments, and so on? Any difference observed between two sample means will be contingent on both the means and the sample standard deviations

REFERENCES

Bluman, A. G. (2009). Elementary statistics: A step by step approach. New York;: McGraw-Hill Higher Education.
Brandt, S., & Brandt, S. (1998). Data analysis. Springer-Verlag. Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test. Psychological bulletin, 57(5), 416.
Woronow, A., & Love, K. M. (1990). Quantifying and testing differences among means of compositional data suites. Mathematical Geology, 22(7), 837-852.

DATA ANALYSIS SECTION

Hypothesis test using z test (n>30).

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.