What does your scatter diagram look like? Briefly describe it.
Based on your prediction about the relationship between the amount of coffee people drink and the number of hours they sleep, what is your H0 and what is your HA ?
Did you find a positive linear relationship or a negative linear relationship? How do you know?
• If you were pursuing this study, what calculations would you perform to describe this relationship and why?
• Without completing the calculations, assume that you obtained a significant result. What would you conclude about the relationship between coffee and sleep?
Did you find a positive linear relationship or a negative linear relationship? How do you know?
If you were pursuing this study, what calculations would you perform to describe this relationship and why?
Without completing the calculations, assume that you obtained a significant result. What would you conclude about the relationship between coffee and sleep?
We have no access to your scatter diagram.
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Marks: 1.00
The value of a correlation is reported by a researcher to be r = -0.5. Which of the following statements is correct?
Choose one answer. a. The x-variable explains 50% of the variability in the y-variable.
b. The x-variable explains -50% of the variability in the y-variable.
c. The x-variable explains 25% of the variability in the y-variable
d. The x-variable explains -25% of the variability in the y-variable
As an AI bot, I don't have the ability to directly generate a scatter diagram or conduct the study you mentioned. However, I can guide you on how to interpret it and what calculations you may need.
To create a scatter diagram, you would typically plot the values of coffee consumption and hours of sleep on a graph with one variable on the x-axis (horizontal) and the other variable on the y-axis (vertical). Each data point would represent an individual's coffee consumption and corresponding hours of sleep.
Based on your prediction about the relationship between coffee consumption and hours of sleep, you would have a null hypothesis (H0) and an alternative hypothesis (HA). The null hypothesis typically assumes no relationship between the variables, while the alternative hypothesis suggests that there is a relationship.
Determining whether the relationship is positive or negative relies on the data points' distribution on the scatter diagram. A positive linear relationship means that as the coffee consumption increases, the hours of sleep also increase. Conversely, a negative linear relationship means that as coffee consumption increases, the hours of sleep decrease. To assess which relationship exists, you would analyze the scatter diagram's overall trend.
To quantify the relationship and provide a more precise description, you could calculate the correlation coefficient. The correlation coefficient measures the strength and direction of the linear relationship between two variables. If the coefficient is close to +1, that indicates a strong positive correlation, whereas a coefficient near -1 indicates a strong negative correlation. A coefficient near zero would suggest a weak or no correlation. Additionally, you could perform a regression analysis to estimate the equation of the line that best fits the data points.
If you obtain significant results, it implies that the correlation or relationship observed between coffee consumption and hours of sleep is unlikely to have occurred by chance. However, without performing the actual calculations, we cannot determine the exact conclusion about the relationship between coffee and sleep.
Remember that these explanations provide the general approach, but the specific statistical methods and calculations might vary depending on the data and research context.