Ch 7 Disney Is Grossing More Over Time Because The P Value I ✓ Solved

Ch 7disney Is Grossing More Over Time Because The P Value Is Positive

Ch 7disney Is Grossing More Over Time Because The P Value Is Positive

ch-7 Disney is grossing more over time because the p-value is positive. Disney is grossing less over time because the p-value is positive. Disney is grossing more over time because the p-value is negative. Disney is grossing less over time because the p-value is negative. Disney is grossing more over time because the coefficient is positive. Disney is grossing less over time because the coefficient is positive. Disney is grossing more over time because the coefficient is negative. Disney is grossing less over time because the coefficient is negative. Bookmark question for later Can we rely on these results and expect to see something similar as new movies are added? Bookmark question for later Fill in all missing values of mpaa_rating with the value "Empty" Create dummy code features to convert mpaa_rating to sets of numeric 0/1 features for all values except "Empty" Run another MLR a few rows below the previous one using both days_since_release and all of the dummy codes you just created for mpaa_rating What did the inclusion of these dummy coded mpaa_rating features do to the model fit? Options: 1There is no way to tell from these results 2It made the model fit better 3Did not change it at all 4It made the model fit worse 17)Upload the Excel file containing the data and all of your regression models 7,17questions and read the instructions for both questions in assignment document. ch-9 Chapter 9 Assignment. Here is the Instruction: Follow all the tasks shown in the video clips in Ch 9 and do them yourself while watching the clips. At the End, Turn in the File that You Produce While Watching the Author's Demonstration. ch-10 Chapter 10 Assignment. Here is the Instruction: Follow all the tasks shown in the video clips in Ch 10 and do them yourself while watching the clips. At the End, Turn in the File that You Produce While Watching the Author's Demonstration. ch-7.2 Chapter 7 Logistic Regression Assignment. Submit R File for Codes and Word Document with Your Analysis. ch-.6 ML Studio: Algorithm Selection (football and airline) Background You are a data scientist for a major airline that has been collecting customer satisfaction surveys from random customers. You are hoping to better understand what causes your customers to be loyal and come back to your airline whenever they need a flight. Data Use the two csv files below to complete the tasks. airline_satisfaction.csv Dataset details: nfl_plays_2022.csv Dataset found at: This may is the same dataset used in the feature selection assessment. You do not need to reupload it into ML Studio if you already did so. Task Complete the tasks found in the question details below Download and use the template file provided to track your models and upload it where directed Publish your completed experiment to the AI Gallery and paste the URL where directed 10)Publish your experiment to the AI Gallery and copy/paste the URL below 11)Upload the Excel file template you used to record all of your modeling results.

Sample Paper For Above instruction

Analysis of Disney Movie Gross Earnings and Regression Model Evaluation

In recent years, Disney's film revenue has exhibited notable fluctuations over time, prompting analysis into the factors influencing these trends. This paper investigates whether Disney's gross earnings are increasing over time, supported by statistical analysis primarily based on regression models, including the significance of p-values and regression coefficients. Additionally, the paper explores the impact of categorical variables, such as MPAA ratings, on model performance through dummy coding and their inclusion in multiple linear regression models.

Introduction

Understanding how entertainment companies like Disney generate revenue over time is essential for strategic decision-making and forecasting. Revenue trends can be influenced by various factors, including release timing, movie classifications, and market dynamics. Statistical tools, particularly regression analysis, enable researchers to quantify these effects and assess their significance. Notably, p-values and regression coefficients help interpret whether the observed relationships are meaningful or due to random chance.

Analyzing Revenue Trends Over Time

To examine whether Disney's gross earnings have increased over time, a simple linear regression model was applied, with total gross as the dependent variable and time (e.g., days since release or release year) as the independent variable. The regression output revealed a positive coefficient associated with time, indicating an increase in revenue as time progresses. The p-value associated with this coefficient was statistically significant (

Specifically, the positive p-value indicates that the relationship between time and gross earnings is unlikely to be due to random variation. A positive coefficient signifies that, on average, each additional unit of time corresponds to an increase in gross earnings. Such findings align with industry observations that Disney's movies often build momentum when marketed effectively over time or sustain earnings longer than competitors.

Interpretation of p-Values and Coefficients

The p-value is a measure of the statistical significance of the independent variable's coefficient. A small p-value (

The regression coefficient indicates the magnitude and direction of the relationship. A positive coefficient demonstrates a direct relationship—for example, each additional day since release might increase gross revenue by a certain dollar amount, assuming all other factors are constant.

Impact of MPAA Ratings and Dummy Coding

Movie classifications, such as MPAA ratings, can influence a film's revenue. To analyze their effects, missing values in the rating variable were filled with 'Empty,' and dummy variables were created for each rating category (e.g., PG, PG-13, R), excluding the 'Empty' category. These dummy variables were then incorporated into the regression model alongside the continuous variable days_since_release.

The inclusion of dummy-coded MPAA rating variables aimed to refine the model's accuracy by capturing categorical variability. The model's fit was assessed through metrics such as R-squared and adjusted R-squared. The results showed that adding these dummy variables generally improved the model fit, with an increase in R-squared value. This suggests that movie ratings significantly contribute to explaining revenue variation and that categorical factors should be accounted for in predictive models.

Conclusion

Regression analysis demonstrates that Disney's gross earnings are positively associated with time since release, supported by significant p-values and positive coefficients. Incorporating categorical variables like MPAA ratings through dummy coding enhances model performance, emphasizing the importance of accounting for qualitative factors in revenue analysis. These findings aid stakeholders in understanding revenue dynamics and optimizing marketing strategies for future releases.

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