BUSN 661 WEEK 3 HOMEWORK RESPONSE

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Feb 20, 2024

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Question C-1 Changing counts to percentages of row or column totals in MS Excel for crosstab analysis to find correlations between variables has various benefits: 1. Simpler Category Comparison: Regularizing counts with percentages regulate data. This shows that regardless of the total number of observations in a category, the sum of percentages inside each row or column will always equal 100%. This makes it easy to compare category answers across different levels of the other variable. 2. Finding Relative Trends: Comparing categories with large sample sizes can be deceptive with raw counts. The relative proportion of replies within each category shows trends and patterns that simple counts may miss. 3. Overall dispersion Control: Percentages account for observation dispersion across categories. If one category has a substantially greater sample size than another, comparing raw numbers would unjustly favor it. Because percentages account for this, you may study category distributions separately. 4. Visualizing Relationships: Percentages improve data visualization. Percentage charts and graphs are easier to understand and show category distinctions than raw count charts. Establishing relationship between variables: After converting counts to percentages, there are numerous approaches to determine if variables are related: 1. Visual Inspection: Check for percentage patterns and trends across both variables' levels. Do certain categories of one variable have considerably greater or lower percentages than others? This may suggest a link. 2. Statistical Tests: Use Chi-square or Fisher's exact tests for autonomy for harder analysis. These tests compare the observed percentage distribution to the anticipated distribution if the variables were independent and estimate the probability of the observed pattern being random. 3. Analysis, including visual examination and statistical testing, should be interpreted in light of the study topic and data restrictions. Consider the observed effect's size and practical ramifications, not just statistical significance. Remember that converting counts to percentages is only one step in the categorical variable analysis. Visual examination, statistical testing, and contextual interpretation aid data-driven decision-making. Question C-2 In the absence of any relationship between the variables, what follows will appear presented in a crosstab having two "Yes/No" type variables where the number of rows is presented as fractions of row totals: The proportion of "Yes" answers in every group of the following parameter will match the proportion of "Yes" replies in all categories combined.

For instance, suppose that 60% of the entire set of replies are categorized as "Yes" in general. If there is no correlation between the variables, the proportion of "Yes" replies for each category of the later factor in every row of the crosstab should likewise equal 60%. The average number of "Yes" and "No" replies for the added variable will be unaffected by the first variable inside every column of the crosstab. Consequently, the proportions of "Yes" and "No" answers for each category of the second variable will be unaffected by the classification of the first variable in that row. Potential Categories of Relationships: In addition to the scenario where there is no link, if the factors at play are not autonomous, they can only show a limited number of relationship types: According to this relationship, the proportion of "Yes" answers for the second variable goes up when the first variable's category goes in a certain way. The percentage of "Yes" answers for the second variable goes down when the classification of the first variable moves in a particular direction. This is the reverse of a positive correlation. For instance, when considering the variable "Age" with categories "18-25," "26- 35," and "36-45," and the variable "Live in home," there may be an inverse correlation seen. This means that the proportion of "Yes" replies ("Live in the apartment") tends to decrease as the age groups progress from "18-25" to "26-35" to "36-45." In certain instances, even when there is a real underlying connection among the variables, the trend may be difficult to see due to tiny or other constraints in the data. The percentages may show an inconsistent correlation, displaying a dispersed or haphazard arrangement. C-3 You can use several analyses of correlation to corroborate your concerns regarding spending on advertising and future sales: 1. Lagged correlation: With this strategy, one may determine the association between the amount spent on advertising in a certain month (let's say January) and the sales in the months that follow (let's say February, March, and April). This approach calculates the association between January advertising spending and sales in February, March, and April. Locating them: When analyzing data from months coming up, utilize Excel's CORREL function with the correct lags for each target month. CORREL (A1:A12, B2:B13) compares January advertising spending to February sales. 2. Rolling window correlation: It analyses correlations in a changing window of data. It evaluates the connection between spending on advertising and sales over a specific timeframe (e.g., 3 months) as the time frame advances over the data. Locating them: Data Analysis ToolPak and Excel formulae can calculate rolling windows. 3: Cross-correlation

By considering the possibility of clustering (serial dependence) in both marketing budgets and sales data, this approach enhances statistical rigor. It finds the lag with the greatest association between variables. Locating them: Cross-correlation research requires specific statistical tools like R or Python. Analysis of control groups If possible, compare the company's sales to similar firms without major advertising initiatives. This isolates the advertising effect on sales despite incorporating external influences. Locating them: Collect competition data or use public market research and financial filings. Extra considerations: Seasonality: Sales data seasonality may affect correlation results. Compare data across time or compensate for seasonality before finding correlations. Other influences: Other than advertising, promotional activities, rival behaviors, and economic conditions might affect sales. To an entire picture, evaluate these things. Explore these linkages and other elements to strengthen your argument for or against spending on ads and future sales. Remember that contextualizing correlations and employing several ways enhances the conclusion and informs decision-making. C-4 Utilizing pivot tables in MS Excel may serve as a beneficial instrument for examining client data and figuring out the key demographic factors that influence "yes/no" purchasing patterns. Here is the method to carry out this: 1. Organize and arrange your data: Make sure that your data is structured in a tabular format, with distinct column labels for each demographic characteristic (such as age, gender, income, and location) and a binary column that shows the purchase behavior as either "Bought" or "Didn't buy". 2. Generate a pivot table: Highlight the complete dataset and proceed to the "Insert" tab. Select the "PivotTable" option to create an empty table. Rows and columns are fundamental components of a grid or table. Rows are horizontal lines that run from left to right, while columns are vertical lines that run from top to bottom. They are used to organize and structure data in a systematic manner. Move the "Purchased" field into the "Values" field. This will display the number of clients in every group who made a purchase or did not make a purchase. Moving each demographic variable to the "Rows" field is as simple as dragging and dropping. This will generate distinct categories and subcategories for every demographic group within the pivot table. 4. Examine the proportions represented by percentages:

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Go to the "Mark of Purchase" area, right-click on a cell, and select "Show Values As." To view the proportion of buyers in each demographic group that purchased your goods, choose "Percent of Row Total". 5. Find the main factors that influence or propel something: Analyze the trends and disparities in percentages among various demographic groups. Take "Gender" as an example; compare the proportion of purchases for the two genders and see if there's a noticeable difference. This implies that gender might be a predominant factor influencing purchasing behavior.

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