Data Analysis MCQ Quiz in मल्याळम - Objective Question with Answer for Data Analysis - സൗജന്യ PDF ഡൗൺലോഡ് ചെയ്യുക

Simulations Model:

• Simulations in statistical modeling typically involve generating synthetic or simulated data that mimics real-world data in order to study statistical properties, test hypotheses, or evaluate statistical methods.
• Simulated data is often used when real-world data is unavailable or when it is impractical or costly to collect new data.
• Simulations allow statisticians and data analysts to explore different scenarios, assess the robustness of statistical methods, and make predictions under various conditions.
• Simulated data is generated using mathematical algorithms or statistical techniques to mimic the characteristics of real-world data, and it can be used to perform various types of statistical analyses to draw inferences and make conclusions. 

Hence, the correct answer is Simulations are used to generate random data for statistical analysis.

Explanation - Click to Open Rate (CTOR) is the percentage of people who open an email campaign and also click on a link within that campaign. CTOR compares the number of unique clicks to the number of unique opens of an email. The CTOR calculates the effectiveness of an email campaign

The correct answer is - (a)-(ii); (b)-(i); (c)-(iv); (d)-(iii)

Key Points

• The most commonly used method of computing correlation between two variables — Inter-class correlation
  • Inter-class correlation measures the degree of relationship between two different classes or groups.
  • It is often applied in studies comparing two separate variables.
• ANOVA technique for estimating reliability of a measure — Intra-class correlation
  • Intra-class correlation (ICC) is used to assess the reliability or consistency of measurements made by different observers measuring the same quantity.
  • It uses variance components from ANOVA for calculation.
• Technique for estimating reliability of multiple-trials tests — Coefficient alpha
  • Coefficient alpha (Cronbach's alpha) measures internal consistency, i.e., how closely related a set of items are as a group.
  • Used when tests have multiple trials or parts measuring the same concept.
• Form of reliability pertaining to testers — Inter-tester reliability
  • Inter-tester reliability checks consistency between different testers administering the same test.
  • High inter-tester reliability indicates minimal bias due to the tester.

Additional Information

• Intra-class correlation
  • Useful for studies involving ratings given by multiple judges or repeated measurements by the same rater.
  • Commonly used in reliability studies involving medical, psychological, and educational assessments.
• Inter-class correlation
  • Applies when comparing two different sets of measurements or classes.
  • Less commonly used in reliability studies compared to intra-class correlation.
• Coefficient alpha (Cronbach’s alpha)
  • A value above 0.7 is generally considered acceptable for internal consistency.
  • Widely used in psychometric tests and questionnaires.
• Inter-tester reliability
  • Important when tests are administered by different individuals in clinical, sports, and educational fields.
  • Minimizes variability in test results due to differences in administration style or interpretation.

Pie charts and Bar charts are considered data visualization methods.

Data visualization method:

• It is a graphical method of presenting data
• For this purpose, we use graphical elements like graphs, charts, maps, etc.
• Visualizations tools can be selected based on the size and type of data

​1. Pie charts:

• It is a circle and sector diagram
• The values are shown as part of a 3600 circle
• The values are converted into percentage values before plot them into the chart 

2. Bar charts:

• It uses to show mainly the frequency distribution graphically
• Sometimes we plot the percentage values

• Line, circle, triangle, and pentagon are shapes 
• Line graph, circle and triangle diagram, pentagon graph, etc, are used to represent different data of different format

The Correct Answer is - F-test.

Key Points

• The F-test is specifically designed for conducting Analysis of Variance (ANOVA).
• ANOVA is used to compare the means of three or more samples to understand if at least one of the sample means is significantly different from the others.
• The F-test evaluates the ratio of the variance between the group means to the variance within the groups. A high F-value indicates that the group means are significantly different.

Additional Informationt-test:

• The t-test is used to compare the means of two groups.
• It is not suitable for comparing more than two groups, which is the primary function of ANOVA.
• Therefore, it is not the correct choice for conducting Analysis of Variance.

Chi-square test:

• The Chi-square test is used for categorical data to assess how likely it is that an observed distribution is due to chance.
• It is mainly used in the context of goodness-of-fit tests, independence tests in contingency tables, and homogeneity tests.
• It does not compare means or variances of groups, making it unsuitable for ANOVA.

Z-test:

• The Z-test is used to determine whether two population means are different when the variances are known and the sample size is large.
• It is not used for comparing the means of three or more groups, which is the purpose of ANOVA.

The correct answer is - (A), (B) and (D) Only.

Key Points 

• Range:
• The difference between the highest and lowest values in a distribution.
• It gives a rough idea of the spread but is sensitive to outliers.
• Quartile Deviation (or Interquartile Range):
• Measures the spread of the middle 50% of a distribution.
• It is the difference between the third quartile (Q3) and the first quartile (Q1).
• This measure is less sensitive to outliers compared to the range.
• Standard Deviation:
• Provides a measure of the average distance of each data point from the mean.
• It is the square root of the variance and gives a comprehensive idea of the spread of the data around the mean.
• It's widely used because it is in the same units as the original data and considers all data points.

Additional InformationVariance:

• The average of the squared differences from the Mean.
• It's a precursor to the standard deviation and provides a measure of the spread of data points.

Explanation - robots.txt file is stored in your website’s top-level directory.

Website Admin and SEO Teams use robots.txt file to inform crawlers which pages of the website should be crawled

Classification is used to group respondents to see how they are different from one another such as age, gender, income, class, location of household, etc.

Nominal Scale:

• It is a scale which neither ranks nor measures data and only assigns objects into discrete categories.
• It only helps in distinguishing or classifying data according to different parameters.
• The nominal scale doesn't take into consideration the numeric values or any other ranking categories.
• Some examples which use a nominal scale are which city you live in, gender, religion, etc. 

Therefore, the classification of respondents only on the basis of gender is an application of the Nominal scale.

Ordinal scale:

• It is the 2nd level of measurement that includes ranking and ordering of data without actually knowing the degree of variation between them.
• The attributes of the data can be rank-ordered here.
• Ranking student's overall performance on a scale of 1 to 5 where 1 refers to poor performance and 5 to the best performance.

Interval scale:

• It is a quantitative measurement scale where there is a presence of order and the difference between two variables is measurable.
• It is used to find out the variables that have constant, familiar, and computable differences.
• The only drawback of this measurement scale is that there is no starting point or a true zero value.
• The measurements under this scale are temperature scale, attitude scale, calendar years, time, etc.

Ratio scale:

• This measurement scale not only helps to know the order of variable but also know the different variables along with information of true zero value.
• It is calculated by assuming that the variables have an option for zero, the difference between the two variables is the same and there is a specific order between the options.
• In addition to the ratio scale does everything that a nominal, ordinal, and interval scale can do, it can also establish the value of absolute zero. 
• Mean, mode and median can be calculated using the ratio scale.

These four levels of measurement were described by S. S. Stevens in 1946.

Key Points T-test:

• A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related to certain features.
• It is mostly used when the data sets, like the data set recorded as the outcome from flipping a coin 100 times, would follow a normal distribution and may have unknown variances.
• A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population.

F. Test:

• The F-test is designed to test if two population variances are equal. It does this by comparing the ratio of two variances. So, if the variances are equal, the ratio of the variances will be 1.
• If the null hypothesis is true, then the F test-statistic given above can be simplified (dramatically). This ratio of sample variances will be the test statistic used. If the null hypothesis is false, then we will reject the null hypothesis that the ratio was equal to 1 and our assumption that they were equal.

Multiple regression

• Multiple regression is a general and flexible statistical method for analyzing associations between two or more independent variables and a single dependent variable.
• Multiple regression is most commonly used to predict values of a criterion variable based on linear associations with predictor variables.

Chi-Square test (\(\chi^{2}\)- test):

• Chi-Square Test of Independence determines whether there is an association between categorical variables (i.e., whether the variables are independent or related).
• It is a nonparametric test.
• This test is also known as the Chi-Square Test of Association.
• Testing the significance of the association between two attributes

Hence, In an experimental study, you want to study the effectiveness of the method of teaching A and B. Each group has 50 randomly selected students. The appropriate technique of data analysis will be t-test.

The correct response is B and D only.

Key Points

• p-value is a measure of statistical significance. A lower p-value indicates a stronger rejection of the null hypothesis (no correlation) and hence a more significant correlation.

In this case:

 

• B has a p-value of 0.01, which is the lowest among all options.
• D has a p-value of 0.03, which is lower than A (0.25) and C (0.59).
• Therefore, B and D are the two most significant correlation coefficients because they have the lowest p-values, indicating a stronger rejection of the no-correlation hypothesis.

So the answer is 3) B and D only.