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I need to analyse 3 CSV worksheets with data from an outbound call centre to encourage people to sign up for a loan: leads.csv is a list of contact details that were fed into the call centre's systems for agents to call calls.csv is a list of calls made by the call centre to leads. There may be no calls or multiple calls to some leads. Each call is given an outcome i.e 'interested' (leads with an interested call does not necessarily mean they signed up and one lead may have multiple 'interested' calls) signups.csv is a list of leads who actually signed up (this may or may not have been after an 'interested' call). Each signup was risk assessed and either approved or rejected for a loan. I need to figure out: What was the mean and median number of calls to a lead before that lead was first marked as interested?

Information given: The more recent data from Fast’s monthly customer survey is now available (Customer Survey Data tab). The data are the responses on a scale of 1 to 5 on the dimensions (factors) of quality, ease of use, price and service. For purposes of this analysis, assume the measurement scale of data is interval Directions: Using point and interval estimates to analyze the data. Specifically, you should address differences between regions’ mean scores, i.e. are any shown to be statistically significant. Then present the results of your analysis in a set of four Excel charts (one for each dimension) showing the point estimates and confidence intervals for each region on tab Customer Survey Charts. Your charts should be as close as possible to the example shown.

Use point and interval to get differences between regions’ mean scores and put results of your analysis in a set of four Excel charts (one for each dimension) showing the point estimates and confidence intervals for each region

Using point and interval estimates to analyze the data. Show differences between regions’ mean scores, then put the results in a set of four Excel charts (one for each dimension)

Using point and interval estimates to analyze the data. Specifically, you should address differences between regions’ mean scores, i.e. are any shown to be statistically significant. Present the results of your analysis in a set of four Excel charts showing the point estimates and confidence intervals

Using point and interval estimates to analyze the data. Specifically, you should address differences between regions’ mean scores, i.e. are any shown to be statistically significant

The data are the responses on a scale of 1 to 5 on the dimensions (factors) of quality, ease of use, price and service. For purposes of this analysis, assume the measurement scale of data is interval. a. Using point and interval estimates to analyze the data. Specifically, you should address differences between regions’ mean scores, i.e. are any shown to be statistically significant.

For purposes of this analysis, assume the measurement scale of data is interval. a. Using point and interval estimates to analyze the data. Specifically, you should address differences between regions’ mean scores, i.e. are any shown to be statistically significant

need to create a CI for each region for each factor.The point is to use the CIs to see if regions differ in their mean scores of each factor. Then graph the results

On Customer survey data tab use point and interval estimates to analyze the data. Specifically, you should address differences between regions’ mean scores. create a CI for each region for each factor.The point is to use the CIs to see if regions differ in their mean scores of each factor. Then graph the results