Quantitative method’s for Management Harvard Case Solution & Analysis

Quantitative method’s for Management Case Solution

Recommendations

1. Taking under consideration the usage of MaaS solutions within a day; the company is advised to provide the MaaS solutions in midday as compared to morning and evening.
2. The sales of the new MaaS solutions could be increased by offering coupons, discounts as well as other measures, to increase the brand awareness and drive sales.

AB Testing

A/B testing is essentially an experiment with two variants, which helps in making the business oriented decisions on the basis of the outcomes derived from the data, instead of making forecasts and predictions. Particularly, the A/B testing helps in creating the variations of the products, and helps in deciding whether to discard or conform the decisions in order to make changes. In the experiment, it would be analyzed that if the 15 percent discount bought prepaid ride; minutes differs from 10 percent discount bought prepaid ride minutes.

The Two-Sample assuming unequal Variances test is used to test to evaluate the difference. In the test, the null and alternate hypothesis are as follows:

Null hypothesis (H0) = There is no significant difference in the discount rate of A (10%) and B (15%).

Alternate hypothesis (H₁): There is a significant difference in the discount rate of A (10%) and B (15%).

Using the data analysis tool on MS Excel; the t-test: Two-Sample Assuming Unequal Variance is as selected and the results of the results are as follows;

Rejection Region

α=0.05

The null hypothesis would be rejected if t is less than 0.05 significance level.

Test Statistic:

T stat = -4.936231643

P-value = 0.000001188421

On the basis of the decision rule; the p-value is less than 0.05 significance level, hence providing strong statistical evidence of rejecting the null hypothesis - H₀ as p-value (0.000001188421) < α (0.05).

Conclusion

The Two-Sample Assuming Unequal Variance tests show that the p-value is lower than the 0.05 significance level, hence indicating the strong evidence against the null hypothesis.Therefore, we reject the null hypothesis which states that there is no significant difference in the discount rate of A (10%) and B (15%).

Recommendations

As per the analysis, the company is recommended to keep using both discounts, i.e. 10 percent and 15 percent because of the significant difference in the discount rate of A (10%) and B (15%) for prepaid ride minutes.

The effect size refers to the value of parameter of the hypothetical statistical population that tends to operationalize how parameters or statistics lead towards the value of effect size. The effect size is calculated by taking the mean of the values of group 1 and group 2 and then the pooled standard deviation is calculated by using the formula provided below:

= SQRT (((number of items ordered a – 1) * SD of group a^2+(number of items ordered b – 1)* SD of group b^2)/(number of items ordered a+ number of items ordered b-2))

The effect size is 0.18,which might be impressive as it would not be considered even the smallest effect.(Cheung, 2016).

Regression

The regression analysis would be used to assess whether the predictors would help in understanding how many ride in minutes get sold within a year. In order to evaluate that how many ride in minutes get sold within a year; the regression analysis of the data set  provided would be perform on excel.

There are various reasons of using the regression analysis which are provided below;

1. One of the core reasons of using the regression analysis is that it tends to provide valuable insights and provide a way to the businesses to look into the near future.
2. Additionally, the regression analysis helps in evaluating the relationship between dependant and independent variables.
3. Not only this, the analysis goes far beyond the correlation due to the fact that it adds the prediction capabilities.
4. It is one of the widely used statistical methodology that is used to estimate the direction and the strength of the relationship between two or more than two variables.

In addition to this, in the simple regression analysis; the dependant variable is one that is going to be predicted and is denoted as y. On the other hand, the independent variable also known as predictor or the explanatory variable and is denoted as x.............

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