Question 3:
In a certain country it is known that the time it takes to feel better after a certain illness is normally distributed with a mean of 7 days, and a standard deviation of 1.55 days.
A researcher reported that she treated 89 patients, and the average duration it took to heal is 6.67.
Based upon that information, is there a strong enough evidence to conclude that the actual healing time is actually shorter than what is currently known?
Check under 5% level of significance and under 1% level of significance.
Answer 3:
Set of hypotheses:

Since the population’s standard deviation is known, we need to use Z-test and not t-test.
There’s the Statistic value–

From the Z-table the relevant P-value for that left-tailed hypothesis –

Conclusion:
At 5% level of significance, there’s strong enough evidence to reject the null hypothesis, however at 1% level of significance, there’s no strong enough evidence to reject the null hypothesis.

- AIC
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- Coefficient of Determination
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- confint()
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- floor()
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- Independent Variable
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- Measures of Dispersion
- Normal Distribution
- P-value
- Pearson Correlation Coefficient
- plotly
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- xlim
- ylim
- Z-score
- AIC
- all()
- anti_join
- any()
- BIC
- Binomial Distribution
- caTools
- Chi Square test for Goodness of Fit
- Chi Square Test for Independence of Variables
- Coefficient of Determination
- colorRampPalette
- Confidence Interval
- Confidence Interval for One Sample's Proportion
- confint()
- Confusion Matrix
- Contingency Tables
- Continuous Frequency Tables
- Data Visualization
- Data Wrangling
- data.frame
- Dependent Variable
- Descriptive Statistics
- Discrete Frequency Tables
- dplyr
- Expected Value
- Explanatory Variable
- facet_grid
- flextable
- floor()
- floor()
- Frequency Tables
- Geometric distribution
- ggplot2
- glm
- glm()
- Hypothesis Testing
- Hypothesis Testing for a single proportion
- Independent Variable
- Inferential Statistics
- IV's and DV
- Jamovi
- Level of Significance
- Logistic Regression
- Measures of Central tendency
- Measures of Dispersion
- Normal Distribution
- P-value
- Pearson Correlation Coefficient
- plotly
- Plots – Bar Plot
- Plots – Box Plot
- Plots – Density plot
- Plots – Dot Plot
- Plots – Histogram
- Plots – Scatter Plot
- Plots – Violin Plot
- Poisson Distribution
- Practice Questions with answers
- predict()
- Predicted Value
- Probability
- Probability Function
- Probability Theory
- QQ-Plot
- R commands
- R functions
- R Packages
- Randomized Number
- Regression
- Regression Line
- Response Variable
- Sample Size
- sample_frac
- sample_split
- scale()
- scale()
- set.seed
- Simple Linear Regression
- Simple Linear Regression
- Special Probability Distributions
- Standard Deviation
- subset
- summary()
- T test for independents samples means
- test statisticstest
- Trend Line
- Uncategorized
- Variables
- Variance
- xlim
- ylim
- Z-score
- AIC
- all()
- anti_join
- any()
- BIC
- Binomial Distribution
- caTools
- Chi Square test for Goodness of Fit
- Chi Square Test for Independence of Variables
- Coefficient of Determination
- colorRampPalette
- Confidence Interval
- Confidence Interval for One Sample's Proportion
- confint()
- Confusion Matrix
- Contingency Tables
- Continuous Frequency Tables
- Data Visualization
- Data Wrangling
- data.frame
- Dependent Variable
- Descriptive Statistics
- Discrete Frequency Tables
- dplyr
- Expected Value
- Explanatory Variable
- facet_grid
- flextable
- floor()
- floor()
- Frequency Tables
- Geometric distribution
- ggplot2
- glm
- glm()
- Hypothesis Testing
- Hypothesis Testing for a single proportion
- Independent Variable
- Inferential Statistics
- IV's and DV
- Jamovi
- Level of Significance
- Logistic Regression
- Measures of Central tendency
- Measures of Dispersion
- Normal Distribution
- P-value
- Pearson Correlation Coefficient
- plotly
- Plots – Bar Plot
- Plots – Box Plot
- Plots – Density plot
- Plots – Dot Plot
- Plots – Histogram
- Plots – Scatter Plot
- Plots – Violin Plot
- Poisson Distribution
- Practice Questions with answers
- predict()
- Predicted Value
- Probability
- Probability Function
- Probability Theory
- QQ-Plot
- R commands
- R functions
- R Packages
- Randomized Number
- Regression
- Regression Line
- Response Variable
- Sample Size
- sample_frac
- sample_split
- scale()
- scale()
- set.seed
- Simple Linear Regression
- Simple Linear Regression
- Special Probability Distributions
- Standard Deviation
- subset
- summary()
- T test for independents samples means
- test statisticstest
- Trend Line
- Uncategorized
- Variables
- Variance
- xlim
- ylim
- Z-score