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Mathematics & Economics
Statistics Project
English (U.S.)
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Hypothesis Testing and Type Errors (Statistics Project Sample)


Welcome to the final SLP. In this assignment, you will use regression and ANOVA to analyze your data. Write a 2- to 4-page paper summarizing the following points.
Take your data and arrange it in the order you collected it. Count the total number of observations you have, and label this number N. Then create another set of data starting from one and increasing by one until you reach N. For example, if you have 10 observations, then your new set of data would be (1, 2, 3, 4, 5, 6, 7, 8, 9, 10). This set of data is called a time series. Run a regression using your original set of data as your dependent variable, and your time series as an independent variable. Go to the following site (using Google or Firefox) to calculate your regression: What is the regression equation? Interpret and explain your results.
Divide your data in half (or into two groups of 8 or less). Then use ANOVA to test if there is a significant difference between the two halves of your data. Use this site to input your data ( Interpret and explain your results.
Summarize your conclusions at the end of the paper.


Regression analysis
Coefficient of co-relation (multiple R) + 0.1132
Coefficient of determination (R squared) + 0.0128
The regression analysis focuses on determining whether there is a pattern characterizing the data on time spent talking focusing on 16 observations. In any case, regression analysis helps to establish whether there is a relationship between the predictors, which are the independent variables and the dependent variables. This is based on the assumption that it is possible to predict observed data through fitting a model. As such, the predicted regression equation showed that the y intercept was 42.33 minutes. This was within the predicted range of 40-50 minutes. However, the regression analysis also highlights the coefficient of correlation and determination to indicate the strength of relationship between the variables.
Since the coefficient correlation is 0.1132 this shows that there is a weak relationship between dependent variable representing the days and time spent talking on the phone. One possible explanation of this is that the days cannot explain the variations in time spent talking on the phone. Values that lie between 0.1 and 0.5 have a weak positive relationship, but those below 0.1 show no relationship. On the other hand as the value of correlation coefficient nears 1, the relationship between the variables gets stronger. There is no distinction between the values of x and y in correlation analysis. In this case, the prediction ability of the regression analysis may not necessarily be sued to make predictions on the existence of a causal relationship. As such, looking at the unique factors that cause variations in time spent talking on the phone would provide more information. However, this is not a quantitative exercise, but the regression analysis is still useful to determine patterns.
The coefficient of determination also lies between 0 and 1, and a value of 0 indicates that no relationship exists between the variables, while 1 indicates a perfect l...
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