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Introduction
Our main objective of this study is to understand the basic concepts of statistics, its descriptive and inferential statistics. Totally there are four questions that was answered in this study with first question uses frequency distribution and also histogram to assess the distribution of the variable taken into consideration. The second question uses Pearson correlation and simple linear regression to determine the relationship between demand and unit price. The third question deals with one way ANOVA to determine whether there is a significant difference in the mean increased productivity among the four products. Finally, the fourth question involves multiple regression analysis to see whether Advertising and sales significantly influences the price
Question 1
    Class Interval
    Frequency
    Cumulative Frequency
    Relative Frequency
    Cumulative Relative Frequency
    Percentage
    50 - 60
    3
    3
    0.15
    0.15
    0.15
    60 - 70
    2
    5
    0.1
    0.25
    0.1
    70 - 80
    6
    11
    0.3
    0.55
    0.3
    80 - 90
    4
    15
    0.2
    0.75
    0.2
    90 - 100
    5
    20
    0.25
    1
    0.25
    Total
    20
    
    
    
    
About 15% of the student’s examination scores falls between 50 and 60 marks, 10% of the student’s examination scores falls between 60 and 70 marks, 30% of the student’s examination scores falls between 70 and 80 marks, 20% of the student’s examination scores falls between 80 and 90 marks and 25% of the student’s examination scores falls between 90 and 100 marks. From the above histogram, it is found that the distribution of examination scores follows normal distribution approximately
Question 2
2.1. The sample size for this study is 41
2.2. The value of F test statistic is
The p – value of f test statistic corresponding to (1, 39) degrees of freedom is 0.169
Here, the p – value of f test statistic is greater than 0.05, indicating that there is no statistical evidence to conclude that demand and unit price are related
2.3.
The coefficient of determination is 0.0504, indicating that, about 5.04% of the variation in the dependent variable is explained by the regression model while the remaining 94.96% left unexplained
2.4.
Here, Correlation coefficient =
The correlation coefficient is positive and falls between 0.2 and 0.4, we can say that there is a weak positive linear relationship between demand and unit price
2.5.
The regression equation is
Demand = 54.076...