Refuting Advertising Claims: A Data-Driven Analysis for Sales

1 Activity 1 Student Name University Affiliation Professor Date of Submission

2 Activity I Sketch how you might refute this claim using the following: A theoretical argument Theoretically, advertising impacts profitability rate; acquiring new customers assists in the development and expansion of a firm. A firm can thus sell more items and services to its client's thanks to this support. These will make it essential to increase advertising spending to generate profits (Bayer et al., 2020). The method adopted is unpopular if a business invests more money in marketing its goods. It will lower sales instead of increase earnings, ending in ineffective advertising. Data Snapchat lost approximately $750 million due to airing a viral ad that prompted users to either slap Rihanna or fight Chris Brown. The venture ultimately led to a significant loss for the organization rather than a benefit, indicating how marketing may collapse despite investments in the project. Why might the refutation using data be more convincing? While disputing the manager's claim, data is the most compelling argument. Because the information invalidates the manager's claim with some other assertion, it is more convincing than the theoretical argument (Bayer et al., 2020). The findings will then be confirmed using data. Consider comparing the organizations' earnings before and after the advertising was aired. Activity II To produce the weekly drinks, they should take four elements into account. The information comprises sodas in stock at the start of the week, their prices, locations, and if the week has a holiday. Hence, if the sodas are priced relatively, more potential customers will buy

3 them. The store's design will also impact sales as if they are closer to the counter, more clients will see them and purchase more. According to the type of week, whether a holiday is celebrated or not, in addition to the fact that it is a holiday during the current season, soda sales will be high since people will be attending parties and events. Sales would be less in the winter compared to the summer. Once the sales pattern is determined, the data is necessary to predict weekly soda sales. Thus, the data generation process includes the following;  The number of sodas available at the start of the week  The cost of each soda  The placement of the sodas in the shop  The type of the week Activity III 1. Calculate these descriptive statistics. Mean of sales We will employ the Excel AVERAGE function to calculate the sales mean. Therefore, mean result is $1,483,403.68. The variance in materials costs Using the VAR.S function, we can get the variance of material costs, and the result is $11,498,301.44. Covariance of labor costs and materials costs Using the COVARIANCE.S function to obtain the variance of labor costs, the covariance between labor and material costs is $3,594,800.69. Mean of labor costs

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4 Using the AVERAGE function, we will calculate the mean labor costs totaling $72,410.42. Total sales Using the SUM function, we will calculate the total sales of $357,500,286. Calculate at least two more descriptive statistics for this dataset. Using the AVERAGE function, we will determine the mean material cost; the output is $23,682.16. We will utilize the SUM function to obtain the total labor costs, which come to $17,450, 911. Activity IV 1 Based on the probability distribution, there is a 24.6% likelihood that the broker will be accurate 50% of the time. Therefore, 5 out of 10 stock predictions are accurate. Thus, the assertion that the stockbroker is accurate 50% of the time is valid since the five correct alternatives have the most excellent chance. 2 A total of 5 accurate picks out of 10 were determined by calculating the predicted amount of correct choices. We can verify the hypothesis by counting the number of correct predictions provided over a year and the frequency of those picks each month (Mishra et al., 2019). However, by dividing every frequency by the whole number of frequencies, we could translate each frequency into a likelihood. 3 I would find the mean probability of the given sample and then compare it with the population mean of 50% utilizing a t-test The null hypothesis is that the prediction of the stockbroker is 50% correct or u = 0.5

5 The alternative hypothesis is that the prediction of the stockbroker is higher than 50% or u > 0.5 (Mishra et al., 2019) Finally, I would apply a one-tailed hypothesis test and then claim that the stockbroker does not predict if the t-stat estimated is greater than the t-value from the provided information in the table.

6 References Bayer, E., Srinivasan, S., Riedl, E. J., & Skiera, B. (2020). The impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value. International Journal of Research in Marketing , 37 (4), 789-804. Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia , 22 (1), 67.

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Activity 1, Sales n Cost

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