In a recent study, a researcher measured the average daily sugar intake (in units of grams) of 5050 individuals and found the mean intake to be 6262 grams, with a known population standard deviation of 1515 grams. The 95%95\% confidence interval for the population mean sugar intake is (57.8,66.257.8, 66.2). Does it seem likely that the actual population mean could be greater than 7070 grams? Briefly explain your reasoning.

No, because 7070 is far above the upper limit of the confidence interval.

No, because the sample mean is only 6262 grams.

Yes, because 7070 is close to the upper bound of the confidence interval.

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1. Introduction to Statistics(0)

Intro to Stats
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Intro to Collecting Data
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Sampling Methods
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2. Describing Data with Tables and Graphs(0)

Visualizing Qualitative vs. Quantitative Data
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Frequency Distributions
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Histograms
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Bar Graphs and Pareto Charts
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Pie Charts
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Frequency Polygons
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Dot Plots
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Stemplots (Stem-and-Leaf Plots)
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Time-Series Graph
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3. Describing Data Numerically(0)

Mean
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Median
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Mode
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Standard Deviation
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Interpreting Standard Deviation
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Percentiles & Quartiles
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Boxplots
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4. Probability(0)

Basic Concepts of Probability
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Complements
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Addition Rule
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Multiplication Rule: Independent Events
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Introduction to Contingency Tables
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Multiplication Rule: Dependent Events
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Bayes' Theorem
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Fundamental Counting Principle
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Counting
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5. Binomial Distribution & Discrete Random Variables(0)

Discrete Random Variables
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Binomial Distribution
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Poisson Distribution
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Hypergeometric Distribution
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6. Normal Distribution & Continuous Random Variables(0)

Uniform Distribution
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Standard Normal Distribution
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Non-Standard Normal Distribution
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7. Sampling Distributions & Confidence Intervals: Mean(0)

Sampling Distribution of the Sample Mean and Central Limit Theorem
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Introduction to Confidence Intervals
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Confidence Intervals for Population Mean
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Determining the Minimum Sample Size Required
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8. Sampling Distributions & Confidence Intervals: Proportion(0)

Sampling Distribution of Sample Proportion
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Confidence Intervals for Population Proportion
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Chi Square Distribution
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Confidence Intervals for Population Variance
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9. Hypothesis Testing for One Sample(0)

Steps in Hypothesis Testing
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Performing Hypothesis Tests: Means
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Performing Hypothesis Tests: Proportions
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Performing Hypothesis Tests: Variance
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Critical Values and Rejection Regions
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Link Between Confidence Intervals and Hypothesis Testing
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Type I & Type II Errors
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10. Hypothesis Testing for Two Samples(0)

Two Proportions
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Two Means - Unknown, Unequal Variance
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Two Means - Unknown, Equal Variance
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Two Means - Known Variance
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Two Means - Matched Pairs (Dependent Samples)
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Two Variances and F Distribution
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12. Regression(0)

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Goodness of Fit Test
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Contingency Tables
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Introduction to ANOVA
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Multiple Comparisons: Tukey-Kramer Test
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Multiple Comparisons: Bonferoni Test
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Two-Way ANOVA
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7. Sampling Distributions & Confidence Intervals: Mean

Confidence Intervals for Population Mean

7. Sampling Distributions & Confidence Intervals: Mean

Confidence Intervals for Population Mean: Videos & Practice Problems

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Problem 115Multiple Choice

In a recent study, a researcher measured the average daily sugar intake (in units of grams) of $50$ individuals and found the mean intake to be $62$ grams, with a known population standard deviation of $15$ grams. The $95 percent sign$ confidence interval for the population mean sugar intake is ( $57.8 comma 66.2$ ). Does it seem likely that the actual population mean could be greater than $70$ grams? Briefly explain your reasoning.

No, because the sample mean is only $62$ grams.

Yes, because the standard deviation is large.

Yes, because $70$ is close to the upper bound of the confidence interval.

No, because $70$ is far above the upper limit of the confidence interval.

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