Math 201 Basic Statistics Regression Analysis
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Math 201 Basic Statistics Regression Analysis INSTRUCTIONS:
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: http://www.meta-calculator.com/online/?panel-
403-regression-input. What is the regression equation? Interpret and explain your results.
2.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
(http://statpages.org/anova1sm.html). Interpret and explain your results
CONTENT:
Math 201 Module 5 SLP Name Institution Course: Math 201 Basic Statistics Instructor: Date: Regression analysis Data set DayTime135238349432540637738834945103111401238133614351533Regression equation: y= 39.6286 - 0.2786 x Coefficient of correlation = -0.2599 Coefficient of determination =0.0676 Explanation The regression analysis focuses on the statistical relationship between the predictor variable (s) and the outcome (Wang & Jain, 2003). In this case, the outcome was the time spent driving in the weekdays from home to work while the team series representing the days was assumed to be the predictor variable. Consequently, the y intercept was 39.63 minutes, and although it appears meaningless, the average time spent on the road to and from work was more likely to be close to 40 minutes. Nonetheless, the main reason for including the constant is that the
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