Skip to main content

MCQ'S based on Basic Statistics (For B. Com. II Business Statistics)

 

 (MCQ Based on Probability, Index Number, Time Series  and Statistical Quality Control Sem - IV)                                                          

1.The control chart were developed by ……       

A) Karl Pearson

B) R.A. fisher

C) W.A. Shewhart

D) B. Benjamin

 

2.the mean = 4 and variance = 2 for binomial r.v. x then value of n is…..

A) 7

B) 10

C) 8

D)9

 

3.the mean = 3 and variance = 2 for binomial r.v. x then value of n is…..

A) 7

B) 10

C) 8

D)9

4. If sample space S={a,b,c}, P(a) = 0.6 and P(b) = 0.3 then P(c)=…..

A)0.6

B)0.3

C)0.5

D)0.1

 

5 Index number is called

A) geometer

B)barometer

C)thermometer

D)centimetre

 

6.  Index number for the base period is always takes as

A)200

B)50

C)100

D)1

 

 

7. Index number are called as….

A)economic thermometer

B)economic barometer

C)social barometer

D)thermometer

 

8. Seasonal variation are….

A)sudden variation

B)short term variation

C)long term variation

D)none of them

 

9. control chart consist of

A)3 control lines

B) 2 control line

C)central line

D)non of them

10. A time series has .....components.

A)one

B)two

C)three

D)four

 

11. which of the following is a variable chart?

A)x bar chart

B)R chart

C)p chart

D)both A and B

 

12. If Laspeyre’s index = 100, Paasche’s index =169 then fisher index is equal to .

A)1330

B)16900

C)130

D)280

13. If Laspeyre’s index = 100, Paasche’s index =144 then fisher index is equal to .

A)122

B)244

C)120

D)280

 

 

14. for standard normal variate the mean and variance are

A)mean = 0 variance = 1

C)mean = 0 variance = 0

B)mean =1 variance = 1

D)mean =1 variance = 0

15. If Laspeyre’s index = 100, Paasche’s index =169 then fisher index is equal to .

A)1330

B)16900

C)130

D)280

 

16.Tickets are numbered from 1 to 50. One ticket is drawn at random, then probability that drawn ticket has a number 7 or it is multiple is ..

A)1/20

B)1/5

C)1/7

D)7/50

 

17.which of the following is an attribute control chart?

A)X bar chart

B)np-chart

C)R-chart

D all the above

 

18. Prosperity, recession, depression and recovery in a business is an example of ..

A)irregular variation

B)seasonal variation

C)cyclical variation

D)secular trend

 

19. Probability of certain event is …..

A)0

B)1

C)0.5

D)-1

20. Prosperity, recession, depression and recovery in a business is an example of ..

A)irregular variation

B)seasonal variation

C)cyclical variation

D)secular trend

 

 (MCQ Based on Measure of central tendency, Dispersion, Correlation and Regression and basic of Statistics sem- III)

 

1. Number of students in a class is an example of --------variable.             

 

    A) Discrete

B) continuous

C) random

D) both A & B

 

 

 

 

 

 

 

2. Method of collecting data from every member of population is called as ….

 

 

    A)Sampling

B)Census

C)SRSWR

D)SRSWOR

 

 

 

3. Data consist with smallest observation 10 occurs 20 times and largest observation 70 occurs 5 time then range of this data is….

 

 

    A)80

B)60

C)15

D)5

 

 

4. Karl Pearson’s correlation coefficient is always lies between……

 

    A) (0 to 1)

B) (-1 to 1)

C) (-1 to 0)

D) (-0.8 to 0.8)

 

5. The mean of data 24, 23, 21, 19, 17, 16 is ….

 

    A) 16

B) 20

C)18

D)22

 

 

6 If we collect blood group of person’s then it is …..

 

  A)Quantitative data

B)Both A & B

C)Qualitative data

D)none of these

 

7. Which sampling is based on equal probability

 

    A) Simple random sampling   

B) Stratified random sampling

 

    C) Systematic sampling 

D) Probability sampling

 

8. If n= 20 and sum of square of deviation of observation taken from mean is 80 then S.D. is ….

 

    A)4

B)2

C)16

D)5

 

9. The mean of data 15, 20, 25, 18, 24, 28, 10 is ….

 

  A) 16

B) 20

C)18

D)22

 

10. If the smallest value in a set is 82 and its range is 4, the largest value is ---

 

    A) 7       

B) 9  

C) 86

D) 6

 

 

 

 

 

 

11. The equation of regression line y on x is Y= 2+0.5X and if we know Value of     X = 4 then estimate value  of Y is ….

 

    A) 2

B)4

C) 1

D)cannot find

 

12. Advertisement and sale have ……

 

  A)Positive correlation

B)negative correlation

 

  C)both A and B

D)no correlation

 

13. Which average is said to be the best average?

 

    A) Mean

B) Median

C)Mode

D) none of them

 

14. The concept of rank correlation was given by:

 

    A)Galton

B)Spearman

C)Karl Pearson

D)Mood

 

15. Which of the following is absolute measure?

 

  A)coefficient of Q.D.

B) Mean

C) coefficient of S.D.

D)Range

 

16. Median of the values 20, 25, 23, 30, 37 is --

 

    A)23

B)25

C)20

D)37

 

 

17…… Divide the data into two equal parts.

 

    A)Mean

B)Median

C)Mode

D) none of these

 

18. if two regression coefficient byx =4 and bxy = (1/16) then correlation coefficient  r is ….

 

  A)-1/2

B)1/4

C)1/2

D)-1/4

 

19. Variance of 5 observations 20, 20, 20, 20, 20 is ----

 

    A)20

B)100

C)5

D)0

 

 

20.Which of the following is advantage of sampling method over census method?

 

    A)more time

B)less cost

C)less Reliable

D)All the above

 

21. Variance is ….. of S.D.

 

  A)Square

B)square root

C)log

D)none of these

 

 

 

 

 

 

22. The estimate of value of X for given Value of Y is obtained by using

 

    A)line of regression Y on X

B)Correlation between X and Y

 

    C) Line of regression X on Y

D) none of them

 

23. Which of the following is a discrete variable?

 

  A)length

B)width

C)age

D)no. of students

 

24.If mean = median = 20 then by empirical relation value of mode is …

 

  A)5

B)10

C)15

D)20

 

 

Comments

Popular posts from this blog

B. Com. -I Statistics Practical No. 1 Classification, tabulation and frequency distribution –I: Qualitative data.

  Shree GaneshA B. Com. Part – I: Semester – I OE–I    Semester – I (BASIC STATISTICS PRACTICAL-I) Practical: 60 Hrs. Marks: 50 (Credits: 02) Course Outcomes: After completion of this practical course, the student will be able to: i) apply sampling techniques in real life. ii) perform classification and tabulation of primary data. iii) represent the data by means of simple diagrams and graphs. iv) summarize data by computing measures of central tendency.   LIST OF PRACTICALS: 1. Classification, tabulation and frequency distribution –I: Qualitative data. 2. Classification, tabulation and frequency distribution –II : Quantitative data. 3. Diagrammatic representation of data by using Pie Diagram and Bar Diagrams. 4. Graphical representation of data by using Histogram, Frequency Polygon, Frequency Curve and     Locating Modal Value. 5. Graphical representation of data by using Ogive Curves and Locating Quartile Values....

Index Number

 Index Number      Introduction  We seen in measures of central tendency the data can be reduced to a single figure by calculating an average and two series can be compared by their averages. But the data are homogeneous then the average is meaningful. (Data is homogeneous means data in same type). If the two series of the price of commodity for two years. It is clear that we cannot compare the cost of living for two years by using simple average of the price of the commodities. For that type of problem we need type of average is called Index number. Index number firstly defined or developed to study the effect of price change on the cost of living. But now days the theory of index number is extended to the field of wholesale price, industrial production, agricultural production etc. Index number is like barometers to measure the change in change in economics activities.   An index may be defined as a " specialized  average designed to measure the...

Non- Parametric Test: Run Test

Non- Parametric Test  A Non-Parametric tests is a one of the part of Statistical tests that non-parametric test does not assume any particular distribution for analyzing the variable. unlike the parametric test are based on the assumption like normality or other specific distribution  of the variable. Non-parametric test is based on the rank, order, signs, or other non-numerical data. we know both test parametric and non-parametric, but when use particular test? answer is that if the assumption of parametric test are violated such as data is not normally distributed or sample size is small. then we use Non-parametric test they can used to analyse categorical data  or ordinal data and data are obtained form in field like psychology, sociology and biology. For the analysis use the  some non-parametric test that are Wilcoxon signed-ranked test, mann-whiteny U test, sign test, Run test, Kruskal-wallis test. but the non-parametric test have lower statistical power than ...

Statistical Inference

 Statistical Inference: The Power of Statistical Inference in Data Analysis Statistical Inference: Drawing Meaningful Conclusions from Data In a data analysis, The statistical inference is a  a powerful tool for drawing meaningful conclusions from a sample of data  and making inferences about a larger population. It enables us to make confident predictions, understand relationships, and uncover valuable insights that can inform decision-making and shape various fields of study. At its core, statistical inference involves using statistical methods to analyse sample data and extend the findings to the broader population. This approach is necessary because it is often impractical or impossible to collect data from every individual or element of interest. Instead, we carefully select a representative sample and employ statistical techniques to infer information about the larger population. The first aspect of statistical inference is estimation. Estimation allows us to estima...

B. Com. I OE - II Practical No. 3 Scatter Plot.

  Practical No. 3   Scatter Plot.  Correlation In previous blog we discussed about the measure central tendency and Dispersion to use to study the variable. the correlation is a statistical concept that allows us to measure and understand the relationship between two or more variables. it provided a valuable information about that variables. e.g. price and demand of commodity, income and expenditure of family, height and weight of group of persons. their we use the relation   between this two variables. in above examples we see the one variable increases other variable is also changes in same or opposite direction.  definition: Correlation is statistical tool which study the relationship between two or more variables. for analysis of correlation various method and techniques are used.  example: i. Demand and supply of product                               ...

Median test

 Non- Parametric test Median test Median test is also a Non-Parametric test and it is alternative to Parametric T test. The median test is used when we are interested to check the two independent sample have same median or not. It is useful when data is discrete or continuous and if data is in small size.  Assumptions:  I) the variable under study is ordinal scale II) the variable is random and Independent. The stepwise procedure for computation of median test for two independent sample : Step I :- firstly we define the hypothesis Null Hypothesis is the two independent sample have same median.  Against Alternative Hypothesis is the two independent sample have different median.  Step II :- In this step we combine two sample data. And calculating the median of combined data. Step III :- after that for testing hypothesis we constructing the (2x2) contingency table. For that table we divide the sample into two parts as number of observation above and below to the ...

Non-Parametric Test 1.Sign test

Types of Non-Parametric Test : the non-parametric test is Broadly divided into three categories as: 1. One-sample Test  2.Two-sample test 3. K-sample test In this blog we discuss the one-sample tests,  there are four types of one-sample non-parametric test as follows:  1. Run Test. 2.Sign Test 3. Wilcoxon Sing-Rank test 4. Kolmogorov-smirnov test. first we discuss the sign test. 1.Sign Test The sign test id one of the very simplest non-parametric test. the test is based on signs (i.e. plus and minus signs) hence is called sign test. the non-parametric test are alternative to the parametric one, therefore the sign test is alternative to parametric T-test, is used to test the median of population instead of mean of population. (i.e. Parametric T-test is used to testing mean of population and sign test is used to testing the median of the population they are alternative to each other when the data follows assumption of T-test we use parametric t-test, but i some situation th...

Statistical Inference Practical: Point Estimation by Method of Moment

 

Statistical Inference I ( Theory of Estimation) : Unbiased it's properties and examples

 📚Statistical Inference I Notes The theory of  estimation invented by Prof. R. A. Fisher in a series of fundamental papers in around 1930. Statistical inference is a process of drawing conclusions about a population based on the information gathered from a sample. It involves using statistical techniques to analyse data, estimate parameters, test hypotheses, and quantify uncertainty. In essence, it allows us to make inferences about a larger group (i.e. population) based on the characteristics observed in a smaller subset (i.e. sample) of that group. Notation of parameter: Let x be a random variable having distribution function F or f is a population distribution. the constant of  distribution function of F is known as Parameter. In general the parameter is denoted as any Greek Letters as θ.   now we see the some basic terms :  i. Population : in a statistics, The group of individual under study is called Population. the population is may be a group of obj...

B. Com. I OE - II Practical No. 4 Karl Pearson's correlation coefficient

  Practical no. 4 Karl Pearson's correlation coefficient  Correlation In previous blog we discussed about the measure central tendency and Dispersion to use to study the variable. the correlation is a statistical concept that allows us to measure and understand the relationship between two or more variables. it provided a valuable information about that variables. e.g. price and demand of commodity, income and expenditure of family, height and weight of group of persons. their we use the relation   between this two variables. in above examples we see the one variable increases other variable is also changes in same or opposite direction.  definition: Correlation is statistical tool which study the relationship between two or more variables. for analysis of correlation various method and techniques are used.  example: i. Demand and supply of product                           ...