hypothesis testing

Determine if there is correlation between worked hours and number of output.

1. Use Linear Regression
2. Test the hypothesis of whether there is significant correlation ? = 5%
3. Interpret the data
4. accept or reject the Ho or Ha

Ho: There is no relationship

Ha: There is relationship You can use online statistic calculator like Social Science Statistics or you can compute in the traditional way

The numbers of hours and outputs are just assumptions

P Value from T Score Calculator This should be self-explanatory, but just in case it's not: your t-score goes in the T Score box, you stick your degrees of freedom in the DF box (N - 1 for single sample and dependent pairs, (N, - 1) + (N2 - 1) for independent samples), select your significance level and whether you're testing a one or two-tailed hypothesis (if you're not sure, go with the defaults), then press the button. If you need to derive a T Score from raw data, then you can find t test calculators here. Report a T-Test Result (APA) T Score: DF Significance Level: O.01 O.05 O.10 One-tailed or two-tailed hypothesis?: O One-tailed O Two-tailed Enter your values above, then press "Calculate". CalculateLinear Regression Calculator This simple linear regression calculator uses the least squares method to find the line of best fit for a set of paired data, allowing you to estimate the value of a dependent variable (Y) from a given independent variable (X). The line of best fit is described by the equation 9 = bX+ a, where bis the slope ofthe line and a is the intercept (i.e., the value of Ywhen X: 0). This calculator will determine the values of b and afor a set of data comprising two variables, and estimate the value of onr any specified value of X. To begin, you need to add paired data into the two text boxes immediately below (either one value per line or as a comma delimited list), with your independent variable in the XValues box and your dependent variable in the YValues box. For example, if you wanted to generate a line of best fit for the association between height and shoe size, allowing you to predict shoe size on the basis of a person's height, then height would be your independent variable and shoe size your dependent variable). XValues YValues [b