Q :- Use below section and provide conclusive remarks ?

Q :-Provide solution to the problem along with some empirical evidence ?

I. Methodology:

In this study, we evaluate the correlation between wildfire rates and two important variables: GDP growth and population increase. The 16 years of observations that make up the dataset for this study are from 2005 to 2020. The provided information does not specifically cite the data's sources.

I. Data:

The following variables are included in the dataset for each year:

• Wildfire Rate (%): Indicates the yearly proportion of affected land area.

• Population Growth (%): Displays the population's annual percentage change.

• Gross Domestic Product Growth (%): Indicates the annual percentage change in the GDP.

The dataset has 15 observations, which is enough to do a simple analysis. A numerical value that is pertinent to the study issue is represented by each variable.

The data spans the years 2005 through 2020, giving researchers a large amount of time to examine the connections between wildfire rates, population expansion, and GDP development. The fact that the data points for wildfire rates are calculated based on observations from 15 years suggests that it may be an aggregate measure, which is crucial to keep in mind.

II. Analysis:

Part 1: Wildfire Rate to Population Growth:

The association between the rate of wildfires and population increase is investigated in the first section of the analysis using a straightforward linear regression model. The findings of the regression analysis suggest that:

• According to the Wildfire Rate (%) variable's coefficient of 9.7778, a 1% rise in the wildfire rate corresponds to a 9.7778% increase in population growth on average.
• The correlation between the rate of wildfires and population increase is statistically significant at a 95% confidence level, according to the coefficient's p-value of 0.0019.
• Although the population growth coefficient is -3.2684 (p-value = 0.1890), it is not statistically significant. Therefore, there is insufficient data to draw the conclusion that population expansion significantly affects the rate of wildfires.

Part 2: Wildfire Rate to GDP Growth:

Another straightforward linear regression model is used in the analysis' second section to look into the connection between the rate of wildfires and GDP growth. The following are the findings of this regression analysis:

• According to the coefficient for the Wildfire Rate (%) variable, which is 6.1742, an increase in the wildfire rate of 1% is generally correlated with an increase in GDP growth of 6.1742%.
• The correlation between the wildfire rate and GDP growth is very statistically significant, as shown by the coefficient's p-value of 5.05E-11.
• Although the GDP growth coefficient is 0.0492, it is not statistically significant (p-value = 0.6948). Therefore, there is insufficient data to prove that GDP development significantly affects the rate of wildfires.

Part 3: Multiple Regression: Wildfire Rate to GDP Growth and Population Growth:

The association between the wildfire rate, GDP growth, and population increase is examined in the third section of the analysis using a multiple regression model. The following are the findings of this regression analysis:

• According to the coefficient for the Wildfire Rate (%) variable, a 1% rise in the wildfire rate corresponds to a 9.8772% increase in the dependent variable.
• Although the population growth coefficient is -3.3456 (p-value = 0.2267), it is not statistically significant.
• With a p-value of 0.9370, the GDP growth coefficient, which is -0.0104, is also not statistically significant.

Tools/Analytics:

In this investigation, the correlation between wildfire rates, population growth, and GDP growth was investigated using a variety of statistical tools and approaches. The following instruments and methods were employed:

• Simple Linear Regression: To examine the link between a single dependent variable (population growth or GDP growth) and a single independent variable (wildfire rate), simple linear regression was used. This method aids in comprehending the relationship between changes in one variable and those in another.

• Multiple Regression: In order to analyse the association between the rate of wildfires and both population growth and GDP growth at the same time, multiple regression was used. With the help of this method, it is possible to evaluate the combined impact of several independent variables on a single dependent variable.

• Testing for Statistical Significance: To ascertain the significance of the coefficients in the regression models, t-tests, or statistical significance testing, were used. In order to determine if the associations seen in the data were statistically significant or just happened by coincidence, the p-values connected to each coefficient were examined.

• Descriptive Statistics: To summarise the data and acquire a general understanding of the distributions of the variables, descriptive statistics such as means and standard deviations were generated for each variable.

• The disparity between the values predicted by the regression model and the actual observed values was examined using ANOVA (Analysis of disparity). It evaluates the relevance of the regression equation overall and aids in determining the overall fit of the regression model.

• Error Analysis: To determine the precision of the predictions made by the regression model, the standard error of the regression (SER) was determined. The average difference between the actual observed values and the values predicted by the regression equation is calculated.

• Analysis of Coefficients: To ascertain the strength and direction of the correlations between the variables, the coefficients of the regression models were examined. The average change in the dependent variable that results from a one-unit change in the independent variable is shown by the coefficients.

To examine the connections between wildfire rates, population growth, and GDP growth, the tools and methods stated above were used to the dataset that was provided. They contributed to a better understanding of the data and the relevant study topics by helping to quantify the relationships and determine their statistical significance.