Statistical Modelling and Analysis of Economic Data of India

Note: Business product / producing firm = Basmati Rice / basmati rice producing firm- company name does not matter.

Executive summary: [total of 4 marks] Please explain the main features of the assignment including its purpose, utility and expected conclusions.

This report summarise the statistical modelling and analysis results associated with economic data of Country India. The main purpose of report to implemented sampling design in Gross Domestic Products, Household consumptions, Export and Import. Introduction [total of 3 marks]

2.1. Understanding quantitative analysis in economics (1 mark)

2.2. Quantitative analysis: informing business (1 mark)

2.3. The objective of this report (0.5 marks)

2.4. Structure of this report (0.5 marks)

3. Application of Analytical Techniques for Quantitative Analysis [Total of 65 marks]

3.1. Application of basic statistical techniques [25 marks]

3.1.1. Research and present the selected country's GDP, House Hold Consumption, Exports and Imports data for the years 2000 to 2019 in Table 1. [3 Marks]

Table 1: GDP and sectoral contribution in India (in million dollars): 2000-2019 (3 marks) The economy of India is counted as a developing market economy. From year 2000 to 2018 it has been increasing continuously. It is characterized as a world’s one of the largest economy. In a below table data of India’s GDP, Household Consumption, export and import shows the growth of economy.

Year GDP (In Million) House Hold Consumption (In Million) Exports Imports 2000 468,394.95 298,635.06 60,878.40 65,124.16 2001 485,441.03 311,444.01 60,963.53 65,218.39 2002 514,937.96 324,575.63 73,452.73 78,498.58 2003 607,699.30 373,740.77 90,838.37 95,071.65 2004 709,148.53 413,799.14 126,647.72 139,310.02 2005 820,381.67 470,724.54 160,837.84 183,736.13 2006 940,259.89 527,579.31 199,973.92 229,955.03 2007 1,216,735.43 678,457.90 253,077.32 302,803.73 2008 1,198,895.50 679,495.84 288,902.15 350,927.09 2009 1,341,886.70 750,918.25 273,751.84 347,177.60 2010 1,675,615.31 916,978.11 375,353.47 449,974.32 2011 1,823,049.93 1,024,685.66 447,383.95 566,667.15 2012 1,827,637.86 1,031,901.77 448,400.54 571,306.64 2013 1,856,722.12 1,070,321.69 472,180.43 527,555.48 2014 2,039,127.45 1,185,298.23 468,346.04 529,239.68 2015 2,103,587.81 1,241,269.97 416,787.83 465,097.47 2016 2,290,432.08 1,359,101.98 439,642.79 480,169.28 2017 2,652,551.20 1,564,550.22 498,165.36 583,191.72 2018 2,726,322.62 1,621,631.94 536,965.03 638,781.69 2019 0 0 Total 27,298,827.33 15,845,110.01 5,692,549.24 6,669,805.82 Source:

World Development Indicators 2019, the World Bank As per above data, GDP growth has been continuously increasing from year 2000 to 2018. Due to economic expansion of India House hold income increase gradually. While India is dominated more in Import rather than export. During that times (From year 2000 to 2019) import is always more than country’s export. However, demanding in Export and import continuously increasing in that time.

Understanding Quantitative Analysis in Economics

3.1.2. Descriptive statistics of data and interpretation [8 marks]

3.1.2.1. Please present the formula and calculation procedure used to reach the results with reference to each of the four elements of Table 2 (4 elements x 4 Statistics x 0.25 marks = 4 marks).

NUMBER: 19 (From year 2000 to Year 2018) MEAN: It refers as a mean or an average which is used to derive middle tendency of the data. It’s determining by adding the number of data points and diving them in to by all total number. The result is get known as the mean or average. ?Mean=???(a1+a_2+a3?+an)/n? Here, n = sample size For GDP, Mean = 468,394.95+ 485,441.03+ 514,937.96+…+ 2,726,322.62 /19 = 1,436,780.39 For HH Consumption Mean = 298,635.06+ 311,444.01+…+1,621,631.94 / 19 = 1,436,780.39 For Export = 60,878.40 + 60,963.53 + … + 536,965.03 / 19 = 5,692,549.24 For Import = 65,124.16 + 65,218.39 / 19 = 351,042.41 MEDIAN:

The median is the middle number of given data set for GDP, Household Consumption, Export and Import and It is the truly centre of the data set. If n or N is odd, the median is the middle number If n or N is even, the median is the average of the 2 middle numbers Standard Deviation: n = sample size c = number of classes in the frequency distribution m j = midpoint of the jth class f j = frequencies of the jth class 3.1.2.2. Gather the results in table 2. (1 Mark)

Table 2: Descriptive statistics of data from Table 1, 2000-2015 GDP House Hold Consumption Exports Imports Sum 27,298,827.33 15,845,110.01 5,692,549.24 6,669,805.82 Number 19.00 19.00 19.00 19.00 Mean 1,436,780.39 833,953.16 299,607.85 351,042.41 Median 1,341,886.70 750,918.25 288,902.15 350,927.09 Standard Deviation 720,470.89 419,634.52 163,480.88 195,629.94 CV 50% 50% 55% 56% Source:

The Author, 2019. 3.1.2.3. Interpretation of data to inform business decisions (your selected product) (3 Marks): Based on the data above, please interpret the results to inform business decisions in relation to your product.

3.1.3. The coefficient of variation (CV) and the coefficient of correlation. [14 Marks]

3.1.3.1. The coefficient of variation (CV): [7 Marks]

3.1.3.1.1. Based on data from tables 1 and 2, please calculate the Coefficient of Variation for the variables:

HH Consumption, Exports and Imports. Identify the formula (1 Mark) and the calculations for each variable (3 Marks).

Coefficient of variation is always measure of relative variation and it shows variation relative to the Mean. Generally it is Used to Compare Two or More Sets of data Measured in Different Units. It is always measured in percentage. Cv=s/x ? *100 S = Standard Deviation X bar = Mean (Average)

Quantitative Analysis: Informing Business

3.1.3.1.2. Based on the results of 3.1.3.1.1. , please interpret the results to inform business decisions in relation to your product (1 Mark each variable = 3 Marks).

For GDP: For Household Consumption: For Export: For Import: 3.1.3.2. The coefficient of correlation (CC): [7 Marks]

3.1.3.2.1. Based on data from tables 1 and 2, please calculate the Coefficient of Correlation for the variables: HH Consumption, Exports and Imports. Identify the formula (1 Mark) and the calculations for each variable (3 Marks).

Co-relation Coefficient X bar = Mean of X Variable Y bar = mean of Y variable Co-Relation coefficient GDP House Hold Consumption Exports Imports GDP 1.000 0.998 0.969 0.954 House Hold Consumption 0.998 1.000 0.956 0.937 Exports 0.969 0.956 1.000 0.994 Imports 0.954 0.937 0.994 1.000 3.1.3.2.2. Based on the results of 3.1.3.2.1. , please interpret the results to inform business decisions in relation to your product (1 Mark each variable = 3 Marks).

1. GDP – HH Consumption , GDP- Export, GDP Import 2. HH con. – Export , HHC – import 3. Export - import 3.2. Simple Linear Regression [20 Marks] 3.2.1.

Regression Model 1: GDP as the dependent variable and HH Consumption as Independent Variable: Based on the results of table 1, please show the regression model structure to build a relationship between GDP and HH Consumption. (1 Mark).

Regression model structure to build a relationship between GDP and HH Consumption Regression Formula Y= a+bx+€ Y= 7842.302+1.713451X 3.2.2. Based on the results of table 1, calculate the regression model and interpret the results in accordance with the following [19 Marks]: 3.2.2.1. Regression Formula (2 Mark).

3.2.2.2. Table of the Simple Regression Results: Please write the simple regression result formula for model 1 in table 3. (11 Marks).

Table 3. Simple Linear Regression Results (Dependent Variable GDP, Independent Variable: House Hold Consumption). 1 Y-intercept (Constant B0) 7842.302444 2 Regression Coefficient (Slope of Regression Line B1) 1.713451253 3 Random Variable (standard error) 48259.28821 4 R2 0.995749423 Source.

The Author, 2019. 3.2.2.3. Regression line drawing: Please draw a regression line using the E(y) – the expected value of y given x values (2 Marks ). 3.2.2.4.Interpretation of data to inform business decisions: Based on the results of 3.2.2.4., please interpret the results to inform business decisions in relation to your product (4 Marks) 3.3. Multiple Linear Regression [20 Marks]

3.3.1. Regression Model 2: House Hold Consumption as the dependent variable and Export and Imports as Independent Variable: Based on the results of table 1, please show the regression model structure to build a relationship between HH Consumption, Exports and Imports. (1 Mark).

3.3.2. Based on the results of table 1, calculate the regression model and interpret the results in accordance with the following [19 Marks]:

3.3.2.1. Regression Formula (2 Marks). Dependent Variable = Y So, HH consumption = Y Independent variable = X X1 = Export X2 = Import HH Consumption =84605+5.55Export-2.61Import+e 3.3.2.2. Table of the Simple Regression Results: Please write the simple regression result formula for model 1 in table 4. (13 Marks).

Assumption House hold consumption (X) Assumption GDP (Y) GDP (In Million) (From year 2000 to 2018) House Hold Consumption (In Million) (Y) (From year 2000 to 2018) 0 7842.302 468,394.95 298,635.06 100 8013.302 485,441.03 311,444.01 200 8184.302 514,937.96 324,575.63 300 8355.302 607,699.30 373,740.77 400 8526.302 709,148.53 413,799.14 500 8697.302 820,381.67 470,724.54 600 8868.302 940,259.89 527,579.31 700 9039.302 1,216,735.43 678,457.90 800 9210.302 1,198,895.50 679,495.84 900 9381.302 1,341,886.70 750,918.25 1000 9552.302 1,675,615.31 916,978.11 1,823,049.93 1,024,685.66 1,827,637.86 1,031,901.77 1,856,722.12 1,070,321.69 2,039,127.45 1,185,298.23 2,103,587.81 1,241,269.97 2,290,432.08 1,359,101.98 2,652,551.20 1,564,550.22 2,726,322.62 1,621,631.94 0 0 27298827.33 15845110.01

Table 4. Multiple Linear Regression Results (Dependent Variable GDP, Independent Variable: House Hold Consumption). EXPORT IMPORT Y-intercept (Constant B0) 84605 Regression Coefficient (Slope of Regression Line B1) 5.55 -2.61 Random Variable (standard error) 121245 R2 0.929699714 Source.

The Author, 2019. 3.2.2.3. Interpretation of data to inform business decisions: Based on the results of 3.2.2.4., please interpret the results to inform business decisions in relation to your product (4 Marks)

This model is a good model because the R square is 0.9957 which is nearby 1. Which shows its accuracy.

4. Conclusion [3 marks] Students must use own words (and no repetition from the text) to conclude the report. (3 Marks)

5. References [2 marks] Students must use APA referencing as per instruction. (2 Marks) They must write full reference here.

6. Appendix [3 Marks] The student must include all calculation sheets for all tables of data here