|University||The Royal Melbourne Institute of Technology (RMIT)|
|Subject||BUSM 4554: Basic Econometrics|
This assignment uses data from the BUPA health insurance call centre. Each observation includes data from one call to the call centre. The variables describe several characteristics of the call (eg the length of the call, the amount of silence in the call), characteristics of the customer (eg state of residence, family type, number of adults and children), and measures of performance (eg net promoter score, sentiment score of the customer). In this assignment, we are interested in predicting the net promoter score and the length of the call.
1. Calculate descriptive statistics using the ‘summarize’ command for the variables net_promoter_score, total_silence,total_silence_weighted, agent_to_cust_index and agent_crosstalk_weighted and present the results in a table. Comment on what we learn about these variables from the descriptives. Graph a scatter plot of net_promoter_score against agent_crosstalk_weightedand describes the relationship between these two variables.
2. Estimate a multiple linear regression with net_promoter_score as the dependent variable andtotal_silence_weighted, agent_to_cust_indexand agent_crosstalk_weightedas the explanatory (independent) variables. Predict the change in net_promoter_score associated with a 0.1 increase in total_silence_weighted and a 0.01 increase inagent_crosstalk_weighted.Assuming this is the correct model specification, are we sure that total_silence_weighted has a negative effect? [Hint: consider the t-statistic and p-value]
3. Add dummy variables to the regression to control for all of the potential effects of State and Package. Make sure the base category is customers with the “HOSPITAL AND EXTRAS” package in NSW. Carefully interpret the estimated coefficient on the package1 dummy variable you have included. Why is this NOTa very important result?
[Hint: Use the variable labels to include and interpret the correct variables, consider the descriptive statistics of the dummy variables to interpret their importance]
4. Include a quadratic specification of the variable“sentiment_score_cust” in the model along with the existing explanatory variables. Calculate and interpret the marginal effect of a 1 point change in “sentiment_score_cust” when sentiment_score_cust=1 and when sentiment_score_cust=4.
5. Explain the conditional mean independence assumption and assess its relevance with respect to the explanatory variable “sentiment_score_cust”.
[Hint: Think about factors that may be included in the error term of the regression: the customer’s experience with the company (positive or negative), the general attitude of the customer towards call centre conversations (positive or negative), and whether these may be correlated with sentiment_score_cust]
6. Write an executive summary of the findings in questions 2 to 5 on what variables are likely and are not likely to be important drivers of the net promoter score.
“The rise in energy consumption of rapidly growing developing countries, especially China and India, has accounted for the vast majority of the global increase in energy use in recent years. Non-OECD countries currently account for approximately 60% of global energy demand, which is predicted to rise to 70% by 2040 (International Energy Agency, 2014).
This increasing energy use exacerbates environmental problems including global climate change due to greenhouse gas emissions and local environmental problems such as the recent episodes of extreme air pollution in Beijing and other Chinese cities. Besides its environmental impacts, increasing energy use also raises questions of national energy supply security.
As the share of world energy use consumed in developing countries increases, it is increasingly important to understand how energy use evolves across the full income continuum from less developed to highly developed countries (van Ruijven et al., 2009).” Csereklyei and Stern (2015) page 633.
In this part of the home assignment, we will be exploring the drivers of total and sectoral energy use across several developed and developing countries. Please use the dataset:“energy_econometrics_data_SIM2060.dta”
7. Countries have a keen interest in exploring the drivers of their sectoral energy consumption, including ELECTRICITY USE IN INDUSTRY. Please examine the log final ELECTRICITY use by INDUSTRY per capita“ln_elec_indus_pc”.
a) Model Design
Present the results of the descriptive statistics in Table (1).
Design TWO regression models to predict“ln_elec_indus_pc”.
(1) One with a linear per capita GDP term (or its logs) [Model 1],
- Presentation of tables and model adequacy: choose which explanator variables to include, and whether to include them as dummies/ logs/ polynomials/ interactions as you feel appropriate.
- Interpret the coefficients including dummies, elasticities or semi-elasticities
- Interpret the statistical significance of these coefficient
(2) one with a quadratic per capitaGDP term (or its log) [Model 2].
- Presentation of tables and model adequacy: choose which explanatory variables to include, and whether to include them as dummies/ logs/ polynomials/ interactions as you feel appropriate.
- What are the major differences compared to model 1?
- Which model do you think is more appropriate (number 1 or 2)? How do you explain the quadratic model?
b) Discuss how you have designed your model with reference to the “Gauss Markov” assumptions and whether these assumptions are likely to be met.
Interpret the results of THREE of your explanatory variables including income per capita, which you consider to be the key drivers of per capita industrial electricity consumption.
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