type: Statistical Analysis

instruction: Calculate the p-value and determine whether to reject or fail to reject the null hypothesis that the mean number of goals in women's and men's international soccer matches is the same. Use a 10% significance level. Save the result to 'result.csv'.

hardness: Hard


type: Statistical Analysis

instruction: Follow the guidelines in tips.md to conduct a hypothesis test on the Business Case dataset, assessing if the weekday influences the number of electric bicycle rentals. Save the results in a file named ab_test_results.csv according to the provided format.

hardness: Easy


type: Statistical Analysis

instruction: Follow the guidelines in tips.md to conduct a hypothesis test on the Business Case dataset, assessing whether the number of rental bikes differs across seasons. Record the results in a CSV file named kruskal_wallis_results.csv according to the specified format.

hardness: Medium


type: Statistical Analysis

instruction: Could you analyze our Business Case dataset to perform a hypothesis test comparing bike rentals across different weather conditions? I need the results formatted and saved in a file named 'weather.csv'  following the requirement in tips.md.

hardness: Hard


type: Statistical Analysis

instruction:
Conduct a chi-square test on the student data to evaluate the association between gender and religion. Present the statistical results in the specified format:

"""
{
    "Chi-square value": [...],
    "P-value": [...],
    "Degrees of freedom": [...]
}
"""

hardness: Easy


type: Statistical Analysis

instruction: In this task, you will use the Quantitative Risk Management dataset to calculate and save covariance matrices for three distinct time periods: "before," "during," and "after." Based on the `epochs_description.txt` file, perform the calculations and ensure that the results are saved according to the format in `template.csv`. Each file should be named `{epoch}_covariance.csv` to match the corresponding time period. Confirm that the matrices are calculated accurately and saved in the correct format.

hardness: Medium


type: Statistical Analysis

instruction: Compute two covariance matrices from the Quantitative Risk Management dataset: the sample covariance matrix from daily percentage price changes (annualized by 252), and the efficient covariance matrix using the Ledoit-Wolf method. Save them as `sample_covariance_matrix.csv` and `efficient_covariance_matrix.csv`, following the format of `template.csv`.

hardness: Medium


type: Statistical Analysis

instruction: In this task, you will calculate several key risk metrics for the portfolio losses using the Quantitative Risk Management dataset provided. Specifically, you need to determine the mean, standard deviation, 95% Value at Risk (VaR), and Conditional Value at Risk (CVaR) for the worst 5% of cases. Ensure that daily portfolio losses are equally weighted. Please fill in the results in `result.csv`, following the format provided.

hardness: Medium


type: Statistical Analysis

instruction: You are tasked with determining the 99% Conditional Value at Risk (CVaR) for our portfolio using historical data from the years 2008 and 2009. Use a Student's t-distribution to perform this analysis. Ensure that your findings are recorded in `result.csv`.

hardness: Hard


type: Statistical Analysis

instruction: Determine the optimal portfolio weights to minimize Conditional Value at Risk (CVaR) at a 95% confidence level using historical stock price data. Save the optimal weights in `result.csv` according to the specified format.

hardness: Medium


type: Statistical Analysis

instruction: For this task, analyze IBM stock using the Black-Scholes model. Calculate the option prices based on the annualized volatility, and also compute the prices using double the historical volatility. Please enter the results into `result.csv`.

hardness: Hard


type: Statistical Analysis

instruction: Please apply a delta hedging approach to our IBM portfolio using European put options. Make sure to compile your results in `result.csv` with the prescribed output format for review

hardness: Hard


type: Statistical Analysis

instruction: For this task, you need to estimate the 95% Value at Risk (VaR) for a stock portfolio across two periods: 2005-2006 and 2007-2009. Start by calculating daily returns, then compute the portfolio returns using equal weights, and find the 95th percentile of the losses. Enter the results into `result.csv` in the required format.

hardness: Medium


type: Statistical Analysis

instruction: Analyze whether there is a significant shift in the relationship between mortgage delinquencies and portfolio returns before and after the 2008 financial crisis using a Chow test. First, split the data at June 30, 2008, and perform OLS regressions for both sub-periods. Then, calculate the Chow test statistic to assess the structural break. Save the results, including the test statistic and p-value (if applicable), to result.csv.

hardness: Medium


type: Statistical Analysis

instruction: Calculate and compare the 30-day rolling volatility for an equally-weighted portfolio of investment banks, both with and without Citibank. Compute daily returns, then calculate the volatilities, and save the results to result.csv using the provided template.

hardness: Medium


type: Statistical Analysis

instruction: Estimate the Conditional Value at Risk (CVaR) at the 99% confidence level for GE stock. Calculate daily losses from historical data, determine the maximum weekly losses, fit a Generalized Extreme Value (GEV) distribution to these maxima, and compute the reserve needed to cover expected maximum weekly losses for a €1,000,000 GE stock holding in January 2010. Save the results in `result.csv` following the provided format.

hardness: Medium


type: Statistical Analysis

instruction: Compute the Pearson correlation coefficient between female illiteracy and fertility based on the tips provided in tips.md. After calculating the correlation coefficient, fill in the result in the provided result.csv template.

hardness: Medium


type: Statistical Analysis

instruction: Run a linear regression using the Fama-French three-factor model to analyze the returns of a portfolio adjusted for risk (Portfolio_Excess) based on the instructions provided in tips.md. After running the regression, print the regression's adjusted R-squared value and write the results in the format specified in result.csv.

hardness: Easy


type: Statistical Analysis

instruction: Calculate the skewness and fourth moment of daily stock returns following the instructions in tips.md. After computing these metrics, input the results into the result.csv template as specified.

hardness: Easy


type: Statistical Analysis

instruction: Use a z-test to calculate the p-value for the hypothesis "the conversion rate of the experimental group is greater than the conversion rate of the control group," and then fill in the results in the format specified in the result.csv file.

hardness: Easy


type: Statistical Analysis

instruction: Perform an appropriate hypothesis test to determine the p-value and decide whether to reject or fail to reject the null hypothesis that the mean number of goals scored in women's international soccer matches is the same as men's, using a 10% significance level. Finally, fill in the results in the format specified in the result.csv file.

hardness: Easy


type: Statistical Analysis

instruction: Perform a Mann-Whitney U test to compare the number of goals scored in FIFA Women's and Men's World Cup matches from 2002 onwards. The test will determine if the average number of goals in women's matches is significantly higher than in men's. If the p-value is less than 0.01, reject the null hypothesis ("reject"); otherwise, fail to reject it ("fail to reject"). Save the results and conclusion in result.csv following the given format.

hardness: Hard


type: Statistical Analysis

instruction: Please use a Two-Sample Proportions Z-Test to compare the adverse effects between the Drug and Placebo groups, utilizing the `proportions_ztest` function. Ensure that the results are documented in `z_test_results.csv` as per the given format.

hardness: Medium


type: Statistical Analysis

instruction: Conduct a two-sided Mann-Whitney U test to assess whether there is a significant difference in the age distribution between the Drug and Placebo groups. Extract the p-value and store the result in a file named age_group_effects_p_value.csv

hardness: Medium


type: Statistical Analysis

instruction: Using the Interquartile Range (IQR) method, detect outliers in the 'Price' and 'No. of People rated' columns of the dataset. For each of these features, calculate the number of outliers, as well as the upper and lower bounds used to identify them. Record the results in a file named result.csv, following the format provided in sample_result.csv.

hardness: Medium


type: Statistical Analysis

instruction: Calculate the Pearson correlation matrix for the columns 'Overall Rating', 'Seat Comfort', 'Staff Service', and 'Food & Beverages' in the Airline Reviews Dataset. Before computing, convert these columns to numeric data types and remove any rows with missing values in these columns. Save the resulting correlation matrix to a file named result.csv, ensuring it follows the same format as the provided sample_result.csv

hardness: Medium


type: Statistical Analysis

instruction: Calculate the correlation between other variables (excluding ‘id’) and the ‘outcome’ column. Find the variable with the highest correlation and fill in the corresponding column name into result.csv according to the format provided in result.csv.

hardness: Medium


type: Statistical Analysis

instruction: It is observed that the beaks of G. scandens birds on Daphne Major appear to have become deeper. Assuming that the means of two groups of data are the same, you need to calculate the probability of observing the difference in means as per the instructions in tips.md. Under the null hypothesis that the means of the two groups are equal, calculate the probability (p-value) of observing the actual difference in means or a more extreme value.Write the calculated results in the format specified in sample_result.csv into the result.csv file.

hardness: Easy


type: Statistical Analysis

instruction: Using the beak length and depth data of G. scandens birds from Daphne Major in 1975 and 2012, compute the beak length-to-depth ratios for each bird in both years. Calculate the mean ratio for each year and compute the 99% confidence intervals using bootstrap replicates (size = 10,000). Finally, write the results into result.csv, following the format specified in sample_result.csv.

hardness: Medium


type: Statistical Analysis

instruction: Please estimate the 99% Conditional Value at Risk (CVaR) for a portfolio using two methods: T distribution and Gaussian Kernel Density Estimation (KDE). Ensure that the results are entered into `result.csv` as per the given format.

hardness: Medium


type: Statistical Analysis

instruction: Please perform a Bootstrap analysis to determine the 95% confidence interval for the reduction in deaths due to handwashing. Enter your findings into `result.csv`, following the provided format.

hardness: Medium


type: Statistical Analysis

instruction: Please calculate the annualized Sharpe ratio for Amazon and Facebook from the dataset "Risk and Returns: The Sharpe Ratio." This ratio measures the additional return per unit of risk, with the annual factor being the square root of 252. Enter your results in `result.csv` as specified.

hardness: Medium


type: Statistical Analysis

instruction: Analyze the returns of FAANG stocks from the dataset. Calculate the expected annual return and annualized Sharpe ratio for an equally weighted portfolio of these stocks. Save the results in result.csv according to the provided format

hardness: Medium


type: Statistical Analysis

instruction: Identify the geographic region and fuel source with the highest median CO₂ emissions. Then, within each geographical region, perform ANOVA tests to determine if there are significant differences in CO₂ emissions among the different fuel sources. If the ANOVA results are significant, conduct pairwise t-tests between fuel sources within each region, applying the Bonferroni correction for multiple comparisons. Record your analysis results in the file named `anova_results.csv`, following the provided format.

hardness: Hard


type: Statistical Analysis

instruction: Analyze CO2 emissions from various fuel sources across different geographical regions. Compute the Bonferroni corrected P-values and compare fuel sources. Save the results in `bonferroni_corrected_p_values.csv` using the provided template.

hardness: Medium


type: Statistical Analysis

instruction: You need to compute the heritability for G. scandens and G. fortis. Then, compute a 95% confidence interval using pairs bootstrap. Heritability is defined as the ratio of the covariance of the trait in parents and offspring divided by the variance of the trait in the parents. Acquire 1000 bootstrap replicates of the heritability using pairs bootstrap for G. scandens and G. fortis (set the random seed to 42). Please save the results into the result.csv file following the provided format.

hardness: Medium


type: Statistical Analysis

instruction: Calculate 10,000 bootstrap replicates to determine the variance in annual rainfall at the Sheffield Weather Station. Please save your findings in 'result.csv'.(Set Random seed as 42)

hardness: Hard


type: Statistical Analysis

instruction: The average strike force of Frog B was 0.71 Newtons (N), and that of Frog D was 0.42 N, for a difference of 0.29 N. It is possible that the frogs strike with the same force and this observed difference was by chance. You will compute the probability of getting at least a 0.29 N difference in mean strike force under the hypothesis that the distributions of strike forces for the two frogs are identical. Please use a permutation test with a test statistic of the difference of means to test this hypothesis and print the p-value. (seed=42). Save the results in the format of sample_result.csv to a file named result.csv.

hardness: Medium


type: Statistical Analysis

instruction: Determine if Frog D and Frog E have similar impact forces. Compute the corresponding p-value and save the result in the format of sample_result.csv to a file named result.csv. (Set the random seed to 42)

hardness: Easy


type: Statistical Analysis

instruction: Perform a bootstrap hypothesis test to determine if Frog B and Frog D have the same mean impact force, without assuming their distributions are identical. Calculate the p-value and save the result to `result.csv`, following the format provided in `sample_result.csv`.

hardness: Easy


type: Statistical Analysis

instruction: Perform a permutation test to evaluate the hypothesis that a House member's party affiliation has no effect on their vote. Use the fraction of Democrats voting in favor as the test statistic. Calculate the p-value for observing a fraction of Democrats voting in favor less than or equal to the observed fraction (153/244). Set the random seed to 42. Save the p-value to a file named result.csv, following the format of sample_result.csv

hardness: Easy


type: Statistical Analysis

instruction: Using the beak depth data (bd_1975 and bd_2012) for G. scandens from 1975 and 2012, generate 10,000 bootstrap replicates of the mean for each year (set the random seed to 42). Calculate the difference in mean beak depth between the two years and compute a 95% confidence interval for this difference. Save the results to result.csv, following the format provided in sample_result.csv.

hardness: Medium


type: Statistical Analysis

instruction: Calculate the sum of squared residuals (SSR) from a regression analysis predicting quarterly minimum portfolio returns based on mortgage delinquencies using data from 2005 to 2010. Save the results in result.csv

hardness: Medium


type: Statistical Analysis

instruction:
You are working on a project for a food industry analytics company. Your task is to analyze the data to determine the average ratings for restaurants offering pickup and delivery, along with the T-Statistic and P-Value. Report your findings with three significant digits and format your response as follows:
"""
{
    "Average rating for pickup restaurants": [3.81],
    "Average rating for delivery restaurants": [3.73],
    "T-Statistic": [2.31],
    "P-Value": [0.0207]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
As a data scientist for a medical research institute, you need to examine the data to find the survival rate statistics, including the number of observations, mean, minimum, maximum, variance, skewness, and kurtosis. Your response should be formatted like this:
"""
{
    "Number of observations (Nobs)": [5191],
    "Mean": [...],
    "Minimum": [...],
    "Maximum": [...],
    "Variance": [...],
    "Skewness": [...],
    "Kurtosis": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
You are working on a project for an aviation industry analytics firm. Your task is to interpret the output of a two-sample t-test comparing the mean number of fatalities between aircraft manufactured by Boeing and Douglas. Provide the conclusion regarding the null hypothesis ("reject" or "accept") and the implications of the findings ("statistically significant difference" or "no statistically significant difference"). Format your response as follows:
"""
{
    "T-statistic": [13.7262535],
    "P-value": [3.196323892903482e-40],
    "Conclusion": ["reject"/"accept"],
    "Implication": ["statistically significant difference"/"no statistically significant difference"]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Calculate the Pearson correlation coefficient to evaluate the relationship between BMI and medical expenses to determine if there is a significant linear relationship. Provide your results in the following format:

"""
{
    "Pearson's correlation coefficient": [...],
    "P-Value": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Perform a chi-square test to assess whether the app category significantly affects the rating distribution. Use a significance level of 0.05. Based on the p-value, select an appropriate conclusion between "Fail to reject the null hypothesis: No significant effect of category on rating distribution." and "Reject the null hypothesis: The category of an app affects its rating distribution." Provide your response in the following format:

"""
{
    "Chi2 Statistic": [...],
    "P-value": [...],
    "result": [...]
}
'""

hardness: Medium


type: Statistical Analysis

instruction:
Conduct a t-test to compare the average ratings of free and paid apps to determine if there is a significant difference. Use a significance level of 0.05. Based on the p-value, choose the appropriate conclusion between "Reject the null hypothesis: Free apps have a higher average rating than paid apps." and "Fail to reject the null hypothesis: No significant difference in ratings between free and paid apps." Provide your answer in the following format:

"""
{
    "T-Statistic": [...],
    "P-value": [...],
    "result": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
With a random seed of 42, randomly select 100 data points from the dataset and conduct a Z-test on Canada's CO2 emission data. Determine if the sample mean significantly differs from the population mean at a 0.05 significance level. Based on the Z-value, choose between "Fail to reject null hypothesis" and "reject null hypothesis." Format your findings as follows:
"""
{
    "Z-value": [...],
    "Sample Mean": [...],
    "Sample Standard Deviation": [...],
    "result": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Perform a t-test to assess whether there is a significant difference in average medical expenses between males and females. Apply a 0.05 significance level. Depending on the p-value, choose between "Fail to reject null hypothesis" and "reject null hypothesis" for the result. Format your answer as follows:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "result": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Perform a t-test to compare the average medical expenses between smokers and non-smokers to determine if smoking status has a significant impact on medical expenses. Use a significance level of 0.05. Based on the p-value, select either "Fail to reject null hypothesis" or "reject null hypothesis" as the result. Provide your results in the following format:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "result": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
From the January 2018 dataset, select samples with a status of 'failed' and 'successful'. Apply a logarithmic transformation to the 'usd_goal_real' feature. Perform a Shapiro-Wilk normality test on the transformed data, calculating both the test statistic and p-value. Based on the p-value, conclude whether the data exhibits a "Normal Distribution" or a "Non-Normal Distribution". Present your findings as follows:

"""
{
    "Shapiro stat": [...],
    "P-value": [...],
    "Conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Use an independent two-sample t-test to determine if there is a significant difference between males and females in their reasons for eating comfort food. With a p-value threshold of 0.05, select the appropriate conclusion from "There is a significant difference" and "There is no significant difference". Provide your response in the following format:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""

hardness: Easy


type: Statistical Analysis

instruction:
Analyze the data to calculate the mean and standard deviation of purchase amounts for two age groups: those over 65 years old and those under 25 years old. Perform an independent two-sample t-test to determine if there is a significant difference in purchase amounts between the two groups. Provide your response in the following format:

"""
{
    "Oldage Purchase Amount Mean": [...],
    "Youngage Purchase Amount Mean": [...],
    "Oldage Purchase Amount std": [...],
    "Youngage Purchase Amount std": [...],
    "T_Statistic": [...],
    "P_Value": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Filter the data for each season by calculating the quartiles and outlier range, excluding any data outside this range. Next, perform an independent two-sample t-test to assess whether there is a significant difference in bike rental counts between weekdays and weekends. Use the resulting p-value to draw a conclusion, choosing from "Accept the null hypothesis" or "Reject the null hypothesis". Present your findings as follows:

"""
{
    "p_value": [...],
    "conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction: For each season, filter the data by calculating the quartiles and outlier range, removing any data outside this range. Next, perform the Shapiro-Wilk test for normality on the filtered data for each season, and record the statistics and p-values. Ensure the results are saved in result.csv with the columns "Season", "Stats", and "P-Value".

hardness: Medium


type: Statistical Analysis

instruction:
Calculate the quartiles and outlier range for each season's data, filtering out the outliers. Then, use the Levene test to compare the rental count variances across the four seasons. Based on the p-value obtained, decide between "Variances are not equal" and "Variances are equal among different seasons". Format your response like this:

"""
{
    "p-value": [...],
    "conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Calculate the quartiles and outlier range for each season's data, filtering out the outliers. Then, use the Kruskal-Wallis test to determine if the number of bike rentals is significantly impacted by the season. Based on the p-value obtained, decide between "Season does impact the count of cycles used" and "Season does not impact the count of cycles used". Format your response like this:

"""
{
    "p-value": [...],
    "conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Filter the data for each season by calculating the quartiles and outlier range, excluding any data outside this range. Then, perform the Shapiro-Wilk normality test to calculate the normality of bike rental counts under different weather conditions (Clear, Misty, Rainy). Based on the p-values obtained, select the final conclusion from "Data is not normally distributed" and "Data is normally distributed". Provide your response in the following format:

"""
{
    "Clear weather P-value": [...],
    "Misty weather P-value": [...],
    "Rainy weather P-value": [...],
    "conclusion": [...]
}
"""

hardness: Hard


type: Statistical Analysis

instruction:
First, filter the data for each season by calculating the quartiles and outlier range, removing any data outside this range. Then, apply the Levene test to assess whether the variances of bike rental counts differ under the three weather conditions (Clear, Misty, Rainy). Use the obtained p-values to draw a conclusion, choosing between "Variances are not equal" and "Variances are equal". Present your findings as follows:

"""
{
    "P-value": [...],
    "conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Filter the data for each season by calculating the quartiles and outlier range, excluding any outliers. Next, use the Chi-square test to assess the correlation between season and weather conditions. Based on the p-values obtained, determine if there is an impact of season on weather. Format your response like this:

"""
{
    "P-value": [...],
    "Conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Use one-way ANOVA to evaluate if there are significant differences in total rating counts (rating_count_tot) among different snapshot screen numbers (ipadSc_urls.num) at a 0.05 significance level. Based on the p-value, determine if "There is a significant difference" or "There is no significant difference". Format your response like this:
"""
{
    "One-way ANOVA P": [...],
    "Conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Use the descriptions from tips.md to calculate the effect size for height and weight for males and females. Provide your response in the following format:

"""
{
    "height's effect size": [...],
    "weight's effect size": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Perform a two-sample t-test to compare the net worth of self-made billionaires and inherited billionaires to determine if there is a significant difference. Provide your response in the following format:

"""
{
    "T-statistic": [...],
    "P-value": [...]
}
"""

hardness: Easy


type: Statistical Analysis

instruction:
Calculate the mean, variance, and standard deviation for the win_by_wickets data according to the instructions in tips.md. Provide your response in the following format:

"""
{
    "Mean": [...],
    "Variance": [...],
    "Standard deviation": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Use a two-sample t-test to calculate the t-statistic and p-value for the technical abilities between left-footed and right-footed players using FIFA20 data. With a p-value threshold of 0.05, choose the appropriate conclusion from "There is a significant difference" and "There is no significant difference". Provide your response in the following format:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Perform a two-sample t-test to assess the shot power of left-footed and right-footed players using FIFA20 data. Calculate the t-statistic and p-value. With a threshold of 0.05, determine if "There is a significant difference" or "There is no significant difference". Format your response like this:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""

hardness: Medium


type: Statistical Analysis

instruction:
Compute the mean, standard deviation, sample size, and standard error for the natural log of total medals for Olympic countries when they are hosting and when they are not hosting. Calculate the standard error between the means by comparing the difference in means to the sum of standard errors. Present your findings as follows:

"""
{
    "Mean Natural Log of Total Medals when Not Hosting": [...],
    "Mean Natural Log of Total Medals when Hosting": [...],
    "Standard Error of Natural Log of Total Medals when Not Hosting": [...],
    "Standard Error of Natural Log of Total Medals when Hosting": [...],
    "Standard Errors between means": [...]
}
"""

hardness: Hard


type: Statistical Analysis

instruction:
Conduct a two-sample t-test to evaluate whether there is a significant difference in the natural log of total medals between countries that host the Olympics and those that do not. Based on a p-value threshold of 0.05, determine the conclusion: "Same distributions (fail to reject H0)" or "Different distributions (reject H0)". Present your findings as follows:

"""
{
    "T-Statistics": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""

hardness: Medium