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Mathematics & Economics
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Math 201 Basic Statistics Regression Analysis (Essay Sample)

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(dot)meta-calculator(dot)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(dot)org/anova1sm.html). Interpret and explain your results

source..
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 mean of the residuals is then zero, and living the constant would result in a biased prediction. The implication of the regression analysis is that for each subsequent day the time spent driving to and from work is likely to decrease by 0.28 minutes. The coefficient of the independent variables (days), indicates the size of the effect on driving time (dependent) variable, and size it is a negative sign it indicates the direction of the effect. The assumption of linear regression makes it easier to evaluate the relationship of the coefficients to the outcome compared to logistic and probit regression (Wang & Jain, 2003).
The Coefficient of correlation was -0.2599 and this assesses how the least squares fit the observed data. In this case, the association of the days in progression to the driving time was a negative correlation showing a weak relationship between the two variables. Even though, the weak relationship shows that the number of days cannot wholly explain the variations in the driving time to and from work, it cannot be ruled out that the time would likely be less as days progressed. The rationale for this is that the coefficient of correlation has a negative sign. In any case, the coefficient is not below 0.1, which shows that the relationship is virtually nonexistent. It is necessary to identify factors that caused variations in driving time, since the coefficient merely helps to identify the strength of the causal relationship. Determining the emerging patterns is possible by taking into account the coefficient and regression analysis.
The coefficient of determination was 0.0676 and is the coefficient of cor...
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