Vielen Dank lieber Bernhard!
Wenn du magst kannst du mir mal deine E-Mail-Adresse mitteilen.
zu1a)
Die Tests sagen mir leider alle nichts. Im Unterricht haben wir nur den Kolmogorov–Smirnov test kurz angesprochen.zu 1aa)
Ich habe wirklich keine Ahnung wie ich aus diesem Text die Hypothesen bilden soll 
zu 1b) Danke, hier habe ich die selben Eigenschaften
zu 1c)
hier habe ich nur Regressionsanalyse aber ANCOVA scheint mir besser geeigent. Danke.
Vielleicht magst du mal über meine Ausarbeitungen zu 2. drüber lesen, ob das so richtig ist:
2a)
Correlation coefficient: In our example, Pearson’s r is 0.925. This number is very close to 1. For this reason, we can conclude that there is a strong relationship between our two variables (sales and employees)
Coefficient of determination: From this it follows that with the used regression model 85,6 percent of company´s sales can be explained by the distribution of number of employees. Or statistically, linear regression explains 85,6% of the variance in the data.
2b)
Regression function: Y= -696,211 * 7,151 * X
Model: • This column shows the predictor variable(s) (here: employees).
• The first variable (constant) represents the intercept – the height of the regression line when it crosses the Y axis.
• In other words, this is the predicted value of sales when all other variables are 0
B: • These are the values for the regression equation for predicting the dependent variable from the independent variable.
• These are called unstandardized coefficients because they are measured in their natural units.
• These estimates tell the amount of increase sales (7.151) that would be predicted by 1 more employee
Std. Error• The standard error is used for testing whether the parameter is significantly different from 0 (here: 1,036) by dividing the parameter estimate by the standard error
to obtain a t-value.
• The standard errors can also be used to form a confidence interval for the parameter
Beta• These are the coefficients that you would obtain if you standardized all of the variables in the regression, including the dependent and all of the independent
variables, and ran the regression.
T&sig:• These columns provide the t-value and 2 tailed p-value used in testing the null hypothesis that the coefficient/parameter is 0
• The coefficient for number of employees (7,151) is statistically significantly different from 0 using because its p-value is 0.000. (choosen the alpha to be 0,05)
