Statistical Analysis and Interpretation Report for Startup Businesses

This report presents a statistical analysis of business data, focusing on startup costs and regression models. The analysis includes frequency distribution tables, histograms, and hypothesis testing using ANOVA and regression analysis. The report examines the relationship between variables, tests hypotheses about startup costs across different businesses, and uses regression to model the data. The results indicate that startup costs are not normally distributed and vary significantly across businesses. Regression analysis reveals a high explanatory power for the model, and hypothesis testing confirms the statistical significance of the regression model and the slope coefficients. The report concludes with interpretations of the statistical findings and their implications for business analysis. This report is provided by a student to Desklib, a platform offering AI-based study tools.

STATISTICS
Student Id
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TASK 1
1) The requisite parameters belonging to descriptive statistics are summarised for various
businesses in tables outlined below.
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2) A) Frequency Distribution Tables
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B) Relevant histograms for each business are represented below.
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3) Two vital observations drawn are as follows.
 Probability Distribution for the startup cost is not normal
Evidence: i) Non-coincidence of central tendency measures for either of the businesses.
ii) Non symmetric distribution of startup costs in histogram
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 Startup costs to depend on underlying business requirements
Evidence: i) Average costs across data for businesses tend to vary
ii)The histograms for different businesses based on sample data do not match or show similar
pattern.
4) Null Hypothesis: Average startup cost across different businesses can be assumed to be same.
Alternative Hypothesis: There exists one business atleast whose startup shows statistically
significant variation from others
ANOVA is used and not T for mean comparison as concerned variables are exceeding 2.
Observation: Significance level (0.05) >p value (0.02)
Result: Reject Ho or null hypothesis
Conclusion: Average startup costs across businesses cannot be taken as same as significant
variation is observed for atleast a particular business.
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TASK 2
1) Regression Output from MS-Excel Software.
Equation for regression model
2) Based on the regression result, R2 attains a value of 0.9932
This is indicative of the high predictive and explanatory power of the model with the
independent variables capable of offering explanation to 99.32% of the visible variation in the
dependent variable. Hence, the regression model could be concluded to have a good fit.
3) Hypothesis Testing
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The relevant component derived from the MS-Excel based regression output is outlined below.
P value (Also denoted by significance F) is 0.00 and hence lesser than α. The net result is that H0
faces a rejection while H1 faces acceptance. Conclusion drawn hints at statistical significance of
the regression model obtained.
4) Interpretation Table
5) The data for the following table has been collected from the MS-Excel based regression
output and the same is represented in the following tabular format.
6) Hypothesis Testing
Using the p value for the slope coefficients from the MS-Excel based regression output and using
significance level of 5%, the following table can be used to determine the output for the
significance of the slope of each of the independent variable.
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7) All slope coefficients have managed to prove their significance in hypothesis testing and
hence no changes in the model originally computed are suggested as it would lead to lowering
of R2.
8) Using the model derived in part (1) and the inputs provided by the question, the following
computation can be performed.
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