Feb 08, 2016 · For each of the coefficients: Intercept Midpoint Age Perf. Rat. Service Gender Degree What is the coefficient's p-value for each of the variables: 0.631 8.66-035 0.944 0.108 0.616 0.00739 0.732 Is the p-value < 0.05? ... Course Hero is not sponsored or endorsed by any college or university. ...
Sep 17, 2014 · For each of the coefficients: Intercept Midpoint Age Perf. Rat. Service Gender Degree What is the coefficient's p-value for each of the variables: 0.000000 0.019362 0.002193 0.000000 0.067125 0.000341 0.477031 Is the p-value < 0.05? yes yes yes yes no yes no Do you reject or not reject each null hypothesis: reject reject reject reject do not reject do not What are …
Jan 10, 2015 · Do you reject or not reject the null hypothesis: What does this decision mean for our equal pay question: For each of the coefficients: Intercept Midpoint Age Perf. Rat. Service Gender Degree What is the coefficient's p-value for each of the variables: Is the p-value < 0.05?
Dec 12, 2016 · Yes Do you reject or not reject the null hypothesis: Reject the N What does this decision mean for our equal pay question: The regressi For each of the coefficients: Intercept What is the coefficient's p-value for each of the variables: 0.6311665 Is the p-value < 0.05?
Recall from Lesson 3, regression uses one or more explanatory variables ( x) to predict one response variable ( y ). In this lesson we will be learning specifically about simple linear regression. The "simple" part is that we will be using only one explanatory variable.
Simple linear regression uses data from a sample to construct the line of best fit. But what makes a line “best fit”? The most common method of constructing a regression line, and the method that we will be using in this course, is the least squares method.
In order to use the methods above, there are four assumptions that must be met:
We previously created a scatterplot of quiz averages and final exam scores and observed a linear relationship. Here, we will use quiz scores to predict final exam scores.
We can use statistical inference (i.e., hypothesis testing) to draw conclusions about how the population of y values relates to the population of x values, based on the sample of x and y values.
A student-run cafe wants to use data to determine how many wraps they should make today. If they make too many wraps they will have waste. But, if they don't make enough wraps they will lose out on potential profit. They have been collecting data concerning their daily sales as well as data concerning the daily temperature.
We can use the slope that was computed from our sample to construct a confidence interval for the population slope ( β 1 ). This confidence interval follows the same general form that we have been using: