A company manufactures safety helmets and inspects 150150 helmets each week for defects. The number of defective helmets found in each of 1010 consecutive weeks is as follows: 55, 88, 66, 44, 77, 99, 1111, 1313, 1010, and 1414. Using a pp-chart, determine whether the process is in statistical control. If not, explain why.

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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
(0)

Frequency Distributions
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Histograms
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Bar Graphs and Pareto Charts
(0)

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
(0)

Median
(0)

Mode
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Standard Deviation
(0)

Interpreting Standard Deviation
(0)

Percentiles & Quartiles
(0)

Boxplots
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4. Probability(0)

Basic Concepts of Probability
(0)

Complements
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Addition Rule
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Multiplication Rule: Independent Events
(0)

Introduction to Contingency Tables
(0)

Multiplication Rule: Dependent Events
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Bayes' Theorem
(0)

Fundamental Counting Principle
(0)

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
(0)

Standard Normal Distribution
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Non-Standard Normal Distribution
(0)

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
(0)

Determining the Minimum Sample Size Required
(0)

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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11. Correlation(0)

Scatterplots & Intro to Correlation
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Correlation Coefficient
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12. Regression(0)

Linear Regression & Least Squares Method
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Residuals
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Coefficient of Determination
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Inferences for Slope
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Prediction Intervals
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Quadratic Regression
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13. Chi-Square Tests & Goodness of Fit(0)

Goodness of Fit Test
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Contingency Tables
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Independence Tests
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Homogeneity Tests
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14. ANOVA(0)

Introduction to ANOVA
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Multiple Comparisons: Tukey Test
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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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8. Sampling Distributions & Confidence Intervals: Proportion

Sampling Distribution of Sample Proportion

8. Sampling Distributions & Confidence Intervals: Proportion

Sampling Distribution of Sample Proportion: Videos & Practice Problems

Video Lessons Practice Worksheet

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Problem 39Multiple Choice

A company manufactures safety helmets and inspects $150$ helmets each week for defects. The number of defective helmets found in each of $10$ consecutive weeks is as follows: $5$ , $8$ , $6$ , $4$ , $7$ , $9$ , $11$ , $13$ , $10$ , and $14$ . Using a $p$ -chart, determine whether the process is in statistical control. If not, explain why.

The process is not in control because one week's proportion exceeds the upper control limit.

The process is in control because all sample proportions are within the control limits.

The process is not in control because the lower control limit is negative.

The process is not in control because the average proportion is above $0.10$ .

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