State the null and alternative hypotheses for Amazon’s shopping cart A/B test for Ordered Product Sales (OPS), the dollar amount earned from sales. (In a later reflection, you will be asked the same question for Total Units Ordered (Units); for the time being, please focus only on OPS.)

Two hypothesis: Null: OPS does not increase. Alternative: OPS increase.

Your Feedback Excellent: Both criteria addressed and correct; presented as a two-sided test Good: Both criteria addressed; presented as a one-sided test Fair: Both criteria addressed; presented as a mixed one-sided and two-sided test Poor: Failed to address one or both criteria and/or answer is wrong in some way other than the direction of the test Skipped: Did not answer; entered nonsense text

Null hypothesis: H0:μcontrolOPS=μtreatmentOPS

Alternative hypothesis:

Ha: μcontrolOPS≠μtreatmentOPS.

Step-by-step explanation:

1)Null Hypothesis: The null hypothesis (H0) is that the average OPS of the control group is not different from the treatment group. This can be expressed as:

H0:μcontrolOPS=μtreatmentOPS

2)Alternative hypothesis: The alternative hypothesis (Ha) is that the average OPS for the control group is different from the average OPS for the treatment group which implies that the average for the treatment group is not the same/equal. It is either more or less. This can be expressed as follows Ha: μcontrolOPS≠μtreatmentOPS.

It is important to note that this is a two-sided hypothesis test, testing for change in either of the direction. Amazon is testing if the control group average is either greater than or less than the average for the treatment group. Using a one-sided test is not correct in this situation. One-sided test is done If Amazon believed that the treatment would yield the same or higher average OPS than the control.

The null hypothesis would be that the average OPS for the control group is either greater than or equal to the average OPS for the treatment group.

This is represented as:

H0: μcontrol OPS≥μtreatment OPS. The alternative hypothesis is that the average OPS is less for the control group than the treatment group:

Ha: μcontrol OPS<μtreatment OPS. Pairing a null hypothesis that average OPS is the same for both groups (H0: μcontrol OPS=μtreatment OPS) with an alternative hypothesis that the average OPS of the control group is less than the average of the treatment group. That is:

Ha: μcontrol OPS<μtreatment OPS is a mixture of a one-sided hypothesis test and two-sided test; this is not correct.

the red lines denote the population of the bacteria after 35 hours. that would be 2,409.24. the y-intercept is the blue line which is 800 because that is the starting point. the equation used to make the graph is 800(1.032)^h, where h is the number of hours.

step-by-step explanation: