Question 1
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Select the right answer.
With the help of inferential statistics, we can :
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Making conclusions from a sample about the population
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Conclude if a sample selected is statistically significant to the whole population or not
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Compare two models to find which one is more statistically significant as compared to the other.
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We can do feature selection, whether adding or removing a variable helps in improving the model or not.
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Hypothesis testing.
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All
Question 2
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Standard Error is the amount of variation in the _________ data. It is related to Standard Deviation as σ/√n, where, n is the _________ size.
Question 3
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A Sampling Distribution is a probability distribution of a statistic (Mean/Median/Mode) obtained through a large number of samples drawn from a specific population.
Question 4
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A Sampling Distribution behaves much like a normal curve and has some interesting properties like :
Question 5
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Central Limit Theorem states that:
When plotting a sampling distribution of means, the mean of sample means will be equal to the population mean. And the sampling distribution will approach a normal distribution with variance equal to σ/√n where σ is the standard deviation of population and n is the sample size.
Question 6
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Greater the sample size, lower the standard error and greater the accuracy in determining the population mean from the sample mean?
Question 7
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No matter the shape of the population distribution, be it bi-modal, right-skewed, etc. The shape of the Sampling Distribution will remain the same (normal curve)?
Question 8
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For a sampling distribution:
The number of samples has to be sufficient (generally more than 50) to satisfactorily achieve a normal curve distribution. We also have to keep the sample size fixed since any change in sample size will change the shape of the sampling distribution and it will no longer be bell-shaped?
Question 9
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As we increase the sample size, the sampling distribution squeezes from both sides giving a better estimate of the population statistic since it lies somewhere in the middle of the sampling distribution (generally).
Question 10
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The confidence interval is a type of interval estimate from the ___________ distribution which gives a range of values in which the population statistic may lie.
Question 11
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The margin of error is a statistic expressing the amount of random sampling error in the results of a survey.
Question 12
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Margin of Error________ the width of Confidence Interval
Question 13
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Which of the following points are true for confidence intervals?
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Confidence Intervals can be built with different degrees of confidence suitable to a user’s needs like 70 %, 90% etc.
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Greater the sample size, smaller the Confidence Interval
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There are different confidence intervals for different sample means. For example, a sample mean of 40 will have a different confidence interval from a sample mean of 45.
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95% Confidence Interval, does not mean that the probability of a population mean to lie in an interval is 95%. Instead, 95% C.I means that 95% of the Interval estimates will contain the population statistic.
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All of the above.
Question 14
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Hypothesis testing lets us identify ________ statistic to be checked against a _________ statistic or statistic of another sample to study any intervention etc.
Answer
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Sample, Population
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Population, Sample
Question 15
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Null hypothesis is a type of hypothesis in which we assume that sample observations are not by chance. They are affected by some non-random situation. It is denoted by H1 or Ha.
Question 16
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Alternate Hypothesis is a type of hypothesis in which we assume that the sample observations are purely by chance. It is denoted by H0.
Question 17
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Hypothesis Testing is done on different levels of confidence and makes use of z-score to calculate the probability.
Question 18
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For a 95% Confidence Interval, anything above the z-threshold for 95% would reject the null hypothesis.
Question 19
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Write down the steps to hypothesis testing.
Question 20
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The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is ________.
Question 21
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p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct.
Question 22
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Low enough p-value is ground for rejecting the null hypothesis. We reject the null hypothesis if the p-value is less than the significance level?
Question 23
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Type-1 error: Type 1 error is the case when we fail to reject the null hypothesis but actually it is false. The probability of having a type-1 error is called beta(β).
Question 24
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Type-2 error: Type 2 error is the case when we reject the null hypothesis but in actual it was true. The probability of having a Type-2 error is called significance level alpha(α).
Question 25
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For Type 1 and Type 2 error:
α= P (Null hypothesis rejected | Null hypothesis is true)
β= P (Null hypothesis accepted | Null hypothesis is false)
Question 26
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Power of test is defined as
P= 1- Type-2 error
= 1 – β
Lesser the type-2 error more the power of the hypothesis test.
Question 27
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For a Z - test:
1. A Z-test is mainly used when the data is normally distributed.
2. We find the Z-statistic of the sample means and calculate the z-score.
3. Z-test is mainly used when the population mean and standard deviation are given.
Question 28
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T-tests are similar to the z-scores, the only difference being that instead of the Population Standard Deviation, we use the Sample Standard Deviation?
Question 29
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Z-tests are statistical calculations that can be used to compare population means to a sample's.
T-tests are calculations used to test a hypothesis, but they are most useful when we need to determine if there is a statistically significant difference between two independent sample groups.
Question 30
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The Degree of Freedom is the number of __________that have the choice of having more than one arbitrary value.
Question 31
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Select the True statement
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1. Greater the difference between the sample mean and the population mean, greater the chance of rejecting the Null Hypothesis.
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2. Greater the sample size, greater the chance of rejection of Null Hypothesis.
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Both
Question 32
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One-sample t-test compares the mean of _________ data to a known value.
Question 33
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Which of the following points are true for One Sample T- test?
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Determine whether the mean of a group differs from the specified value.
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Calculate a range of values that are likely to include the population mean.
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We can run a one-sample T-test when we do not have the population S.D. or we have a sample of size less than 30.
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All of them.
Question 34
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We use a two-sample T-test when we want to evaluate whether the mean of the two independent samples is different or not.
Question 35
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Two-sample T-test is used to:
Question 36
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Points to be noted for two sample T-test are:
1. The groups to be tested should be __________
2. The groups’ distribution should not be highly _________.
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Independent, Skewed
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Dependent, Normal
Question 37
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A Independent Samples t-test compare the means for ______ different groups?
Samples are __________ of each other?
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Two, Independent
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Same, Dependent
Question 38
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A Paired sample t-test compares means from the ______ group at different times?
Samples are _________ on each other?
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Same, Dependent
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Two, Independent
Question 39
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ANOVA is used to determine whether there are any statistically significant differences between the means of ________ independent (unrelated) groups.
Question 40
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A one-way ANOVA has ______ independent variable, while a two-way ANOVA has ______.
Question 41
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Write down the steps to perform ANOVA.
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Write down the answers
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Check them later
Question 42
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Practical applications of ANOVA in modeling are:
Question 43
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The Chi-Square Test determines whether there is an association between _______ variables (i.e., whether the variables are independent or related).
Question 44
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Goodness of fit: It compares two categorical variables to find whether they are related with each other or not.
Question 45
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Test of Independence: It determines if sample data of categorical variables matches with population or not.
Question 46
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Regression analysis is a form of predictive modelling technique which investigates the relationship between a ___________ (target) and __________ variable (s) (predictor).
Answer
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Dependent, Independent
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Independent, Dependent
Question 47
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The regression sum of squares describes how well a regression model represents the modeled data.
A higher regression sum of squares indicates that the model does not fit the data well?
Question 48
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A residual sum of squares (RSS) is a statistical technique used to measure the amount of_________ in a data set that is not explained by a regression model.
Question 49
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Coefficient of Determination (R-Square): It represents the strength of correlation between two variables?
Question 50
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Correlation Coefficients are used to measure how strong a relationship is between two variables?