Statistical Inference - september stats
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What does the sampling distribution of a statistic show?

  • How the sample statistic varies from sample to sample (correct)
  • The fixed relationship of all sample statistics
  • The distribution of the original population
  • The probability of each individual observation
  • All observed values in a random sample are dependent on each other.

    False

    What is the term for the standard deviation of a point estimator?

    standard error

    The difference between the data point $x_i$ and its mean $\bar{x}$ is called a _____ .

    <p>deviation</p> Signup and view all the answers

    Which of the following is NOT a common statistical inference problem?

    <p>Descriptive statistics</p> Signup and view all the answers

    Match the sample statistics with their definitions:

    <p>X̄ = Sample mean or average S² = Sample variance S = Sample standard deviation P̂ = Sample proportion</p> Signup and view all the answers

    A point estimate is denoted by the symbol $\hat{\theta}_n$.

    <p>True</p> Signup and view all the answers

    What does i.i.d stand for in the context of random variables?

    <p>independent and identically distributed</p> Signup and view all the answers

    What is the formula for the sample mean (average) of a set of observations?

    <p>$ rac{1}{n} imes ext{Sum of observations}$</p> Signup and view all the answers

    The sample variance is calculated using $n$ in the denominator.

    <p>False</p> Signup and view all the answers

    What is the formula for the point estimate of variance (sample variance)?

    <p>S² = \frac{1}{n-1} \sum_{i=1}^{n} (X_i - \bar{X})^2</p> Signup and view all the answers

    The best point estimator for the mean is ___ .

    <p>X̄</p> Signup and view all the answers

    Match the following terms to their definitions:

    <p>Point Estimator = A statistic used to estimate a parameter Sample Mean = The average of a sample Population Variance = The variance of an entire population Sample Standard Deviation = A measure of the amount of variation in a sample</p> Signup and view all the answers

    Given a sample of size 10 with observations of weights, what is the first step to calculate the average weight?

    <p>Add all the weights together</p> Signup and view all the answers

    Find the sample average weight of the given brown sugar bags: 27.7, 31.5, 30.9, 29.6, 27.0, 38.1, 32.4, 31.1, 36.7, 28.4.

    <p>30.53</p> Signup and view all the answers

    The standard deviation is the square root of ___ .

    <p>variance</p> Signup and view all the answers

    Which of the following best describes inferential statistics?

    <p>It makes predictions about a population based on a sample.</p> Signup and view all the answers

    The term 'population' in statistics refers to a small segment of the data being studied.

    <p>False</p> Signup and view all the answers

    What denotes the number of statistical units within a population?

    <p>Population size, denoted as N.</p> Signup and view all the answers

    A _____ variable can take on any value within a given range.

    <p>Quantitative</p> Signup and view all the answers

    Match the following statistical terms with their definitions:

    <p>Random variable = A characteristic of any entity being studied. Parameter = A number that describes some aspect of a population. Qualitative variable = Categorical data that cannot be measured numerically. Random sample = A subset of statistical units selected from the population.</p> Signup and view all the answers

    Which expression represents the expected value of a random variable?

    <p>E(X) = Σ xi * P(xi)</p> Signup and view all the answers

    In statistics, the distribution of random variable X changes when random variable Y changes if they are independent.

    <p>False</p> Signup and view all the answers

    What does the notation θ represent in the context of statistics?

    <p>A parameter that describes some aspect of a population.</p> Signup and view all the answers

    What does a smaller p-value indicate in hypothesis testing?

    <p>Stronger evidence against H0</p> Signup and view all the answers

    Failing to reject H0 means there is sufficient evidence to conclude that H0 is true.

    <p>False</p> Signup and view all the answers

    What is a type-I error in the context of hypothesis testing?

    <p>Convicting an innocent defendant</p> Signup and view all the answers

    In hypothesis testing, a p-value condition of p-value < α indicates that H0 is ______ to be true.

    <p>less likely</p> Signup and view all the answers

    Match the following p-value conditions with their corresponding interpretations:

    <p>p-value &lt; α = Reject H0 p-value ≥ α = Fail to reject H0 Small p-value = Strong evidence against H0 Large p-value = Weak evidence against H0</p> Signup and view all the answers

    What does a jury concluding 'not guilty' correspond to in hypothesis testing?

    <p>Failing to reject H0</p> Signup and view all the answers

    In hypothesis testing, the null hypothesis H0 is equivalent to the alternative hypothesis H1.

    <p>False</p> Signup and view all the answers

    Define hypothesis testing in one sentence.

    <p>Hypothesis testing is a process for assessing the significance of evidence provided by sample data against a null hypothesis.</p> Signup and view all the answers

    What does the notation $X ilde{N}(µ, σ^2)$ represent?

    <p>A random variable from a normal distribution with mean µ and variance σ^2</p> Signup and view all the answers

    The sum of two independent normal random variables is also normally distributed.

    <p>True</p> Signup and view all the answers

    What is the expected value of the weighted sum $aX + bY$ if $X$ and $Y$ are independent random variables?

    <p>aµ1 + bµ2</p> Signup and view all the answers

    If $X ∼ N(µ1, σ^2_1)$ and $Y ∼ N(µ2, σ^2_2)$, then $X + Y ∼ N(____, ____ )$

    <p>µ1 + µ2, σ1^2 + σ2^2</p> Signup and view all the answers

    Match the following terminology with their correct definitions:

    <p>Normal Distribution = A continuous probability distribution defined by its mean and variance Independent Random Variables = Two random variables that do not influence each other Weighted Sum = A linear combination of variables where each variable is multiplied by a constant Sample Proportion = The ratio of a certain characteristic in a sample population</p> Signup and view all the answers

    What is the meaning of the notation $X - Y ∼ N(µ1 - µ2, σ^2_1 + σ^2_2)$?

    <p>It indicates the difference between two independent normal variables is also normally distributed.</p> Signup and view all the answers

    The density function for sums of independent normal variables becomes wider as more variables are added.

    <p>True</p> Signup and view all the answers

    What is the function used to describe the sampling distribution of the sum of 25 normal variables?

    <p>N(µ, σ^2/25)</p> Signup and view all the answers

    Study Notes

    Statistical Inference - Basic Concepts

    • Statistics is divided into descriptive and inferential statistics.
    • Population is the set of all individuals or statistical units of interest.
    • Population size is denoted by N.
    • A random variable assigns an outcome to a statistical unit.
    • Variables are categorized as qualitative/categorical and quantitative.
    • A parameter describes an aspect of a population.
    • E(a) = a, E(aX + b) = aE(X) + b, E(aX + bY ) = aE(X) + bE(Y).
    • V(X) >= 0, V(a) = 0, V(X) = E(X^2) - E^2(X), V(aX + b) = a^2V(X).
    • V(aX + bY) = a^2V(X) + b^2V(Y) if X and Y are independent.
    • A random sample is a representative subset of a population.
    • X1,...,Xn is a random sample of size n, its observations are x1,...,xn
    • X1,...,Xn are independent and identically distributed (i.i.d) random variables.
    • E(X1) = E(X2) = ... = E(Xn) = µ and V(X1) = V(X2) = ... = V(Xn) = σ^2.
    • Statistical inference of θ is done using functions Θ̂n = h(X1,...,Xn).
    • Θ̂n is a random variable, its probability distribution is called the sampling distribution.
    • Standard deviation of a point estimator is called standard error: √V(Θ̂n).
    • A point estimator is a sample statistic used to estimate θ.
    • X̄, S^2, S and P̂ are sample mean, variance, standard deviation, and proportion respectively.
    • xi - x̄ is the deviation of the i-th observation from the mean.

    Inference Procedures for Means

    • Point estimator for µ = E(X) is X̄ = (1/n)Σ(Xi).
    • Point estimator for σ^2 = V(X) is S^2 = (1/(n-1))Σ(Xi - X̄)^2.
    • Point estimator for σ = √V(X) is S = √(1/(n-1))Σ(Xi - X̄)^2.
    • Point estimator for p = Y/N is P̂ = Ỹ/n.

    Sampling Distributions

    The central limit theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the original distribution of the population.

    • This applies to sums of normal distributions, where Sums of n normal distributions become more normally distributed as n increases, converging towards a normal distribution.

    Hypothesis Tests - Inference a Decision

    • A hypothesis test is a process for assessing evidence provided by data against the null hypothesis.
    • The null hypothesis, H0, is a statement about the population parameter that we want to test.
    • The alternative hypothesis, H1, is a statement that contradicts the null hypothesis.
    • The p-value measures the strength of sample data evidence against H0.
    • If the p-value is less than the significance level, α, we reject H0.
    • If the p-value is greater than or equal to α, we fail to reject H0.
    • Type I error occurs when we reject H0, but it's actually true.
    • Type II error occurs when we fail to reject H0, but it's actually false.

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    Description

    This quiz covers the foundational elements of statistical inference, including key definitions, properties of random variables, and the distinction between populations and samples. It also touches on the relationships between expectation, variance, and independence in statistics. Test your knowledge of these essential concepts in statistics!

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