diff --git a/your-code/main.ipynb b/your-code/main.ipynb old mode 100755 new mode 100644 index 59b955a..027f30a --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,12 +12,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "# import numpy and pandas\n", - "\n" + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.stats import ttest_1samp\n", + "from scipy.stats import t\n", + "import statsmodels.api as sm" ] }, { @@ -31,11 +34,218 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameJob TitlesDepartmentFull or Part-TimeSalary or HourlyTypical HoursAnnual SalaryHourly Rate
0AARON, JEFFERY MSERGEANTPOLICEFSalaryNaN101442.0NaN
1AARON, KARINAPOLICE OFFICER (ASSIGNED AS DETECTIVE)POLICEFSalaryNaN94122.0NaN
2AARON, KIMBERLEI RCHIEF CONTRACT EXPEDITERGENERAL SERVICESFSalaryNaN101592.0NaN
3ABAD JR, VICENTE MCIVIL ENGINEER IVWATER MGMNTFSalaryNaN110064.0NaN
4ABASCAL, REECE ETRAFFIC CONTROL AIDE-HOURLYOEMCPHourly20.0NaN19.86
...........................
33178ZYLINSKA, KATARZYNAPOLICE OFFICERPOLICEFSalaryNaN72510.0NaN
33179ZYMANTAS, LAURA CPOLICE OFFICERPOLICEFSalaryNaN48078.0NaN
33180ZYMANTAS, MARK EPOLICE OFFICERPOLICEFSalaryNaN90024.0NaN
33181ZYRKOWSKI, CARLO EPOLICE OFFICERPOLICEFSalaryNaN93354.0NaN
33182ZYSKOWSKI, DARIUSZCHIEF DATA BASE ANALYSTDoITFSalaryNaN115932.0NaN
\n", + "

33183 rows × 8 columns

\n", + "
" + ], + "text/plain": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "... ... ... \n", + "33178 ZYLINSKA, KATARZYNA POLICE OFFICER \n", + "33179 ZYMANTAS, LAURA C POLICE OFFICER \n", + "33180 ZYMANTAS, MARK E POLICE OFFICER \n", + "33181 ZYRKOWSKI, CARLO E POLICE OFFICER \n", + "33182 ZYSKOWSKI, DARIUSZ CHIEF DATA BASE ANALYST \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "... ... ... ... ... \n", + "33178 POLICE F Salary NaN \n", + "33179 POLICE F Salary NaN \n", + "33180 POLICE F Salary NaN \n", + "33181 POLICE F Salary NaN \n", + "33182 DoIT F Salary NaN \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 \n", + "... ... ... \n", + "33178 72510.0 NaN \n", + "33179 48078.0 NaN \n", + "33180 90024.0 NaN \n", + "33181 93354.0 NaN \n", + "33182 115932.0 NaN \n", + "\n", + "[33183 rows x 8 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "data = pd.read_csv(\"Current_Employee_Names__Salaries__and_Position_Titles.csv\")\n", + "data\n" ] }, { @@ -47,12 +257,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 \n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "print(data.head())" ] }, { @@ -64,12 +300,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name 0\n", + "Job Titles 0\n", + "Department 0\n", + "Full or Part-Time 0\n", + "Salary or Hourly 0\n", + "Typical Hours 25161\n", + "Annual Salary 8022\n", + "Hourly Rate 25161\n", + "dtype: int64\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "missing_data = data.isnull().sum()\n", + "print(missing_data)" ] }, { @@ -81,12 +333,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.0 5833\n", + "20.0 1901\n", + "10.0 184\n", + "35.0 104\n", + "Name: Typical Hours, dtype: int64\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "employment_counts = data['Typical Hours'].value_counts()\n", + "print(employment_counts)" ] }, { @@ -105,12 +369,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "POLICE 13414\n", + "FIRE 4641\n", + "STREETS & SAN 2198\n", + "OEMC 2102\n", + "WATER MGMNT 1879\n", + "AVIATION 1629\n", + "TRANSPORTN 1140\n", + "PUBLIC LIBRARY 1015\n", + "GENERAL SERVICES 980\n", + "FAMILY & SUPPORT 615\n", + "FINANCE 560\n", + "HEALTH 488\n", + "CITY COUNCIL 411\n", + "LAW 407\n", + "BUILDINGS 269\n", + "COMMUNITY DEVELOPMENT 207\n", + "BUSINESS AFFAIRS 171\n", + "COPA 116\n", + "BOARD OF ELECTION 107\n", + "DoIT 99\n", + "PROCUREMENT 92\n", + "INSPECTOR GEN 87\n", + "MAYOR'S OFFICE 85\n", + "CITY CLERK 84\n", + "ANIMAL CONTRL 81\n", + "HUMAN RESOURCES 79\n", + "CULTURAL AFFAIRS 65\n", + "BUDGET & MGMT 46\n", + "ADMIN HEARNG 39\n", + "DISABILITIES 28\n", + "TREASURER 22\n", + "HUMAN RELATIONS 16\n", + "BOARD OF ETHICS 8\n", + "POLICE BOARD 2\n", + "LICENSE APPL COMM 1\n", + "Name: Department, dtype: int64\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "department_counts = data['Department'].value_counts()\n", + "print(department_counts)" ] }, { @@ -124,12 +431,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Null hypothesis rejected: Hourly wage is significantly different from $30/hr.\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "hourly_wage_data = data[data['Salary or Hourly'] == 'Hourly']\n", + "\n", + "hourly_wage_data = hourly_wage_data.dropna(subset=['Hourly Rate'])\n", + "\n", + "hourly_wage_mean = hourly_wage_data['Hourly Rate'].mean()\n", + "population_mean = 30 \n", + "alpha = 0.05 \n", + "\n", + "t_statistic, p_value = ttest_1samp(hourly_wage_data['Hourly Rate'], population_mean)\n", + "\n", + "\n", + "if p_value < alpha:\n", + " print(\"Null hypothesis rejected: Hourly wage is significantly different from $30/hr.\")\n", + "else:\n", + " print(\"Null hypothesis cannot be rejected: Hourly wage is not significantly different from $30/hr.\")" ] }, { @@ -143,12 +471,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Null hypothesis rejected: Police salaries are higher than last year's mean.\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "police_salary_data = data[(data['Salary or Hourly'] == 'Salary') & (data['Department'] == 'POLICE')]\n", + "\n", + "police_salary_data = police_salary_data.dropna(subset=['Annual Salary'])\n", + "\n", + "police_salary_mean = police_salary_data['Annual Salary'].mean()\n", + "population_mean = 86000 \n", + "alpha = 0.05 \n", + "\n", + "t_statistic, p_value = ttest_1samp(police_salary_data['Annual Salary'], population_mean)\n", + "\n", + "\n", + "p_value /= 2\n", + "\n", + "\n", + "if p_value < alpha and t_statistic > 0:\n", + " print(\"Null hypothesis rejected: Police salaries are higher than last year's mean.\")\n", + "else:\n", + " print(\"Null hypothesis cannot be rejected: Police salaries are not higher than last year's mean.\")" ] }, { @@ -160,29 +512,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Department with the most hourly workers: STREETS & SAN\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The workers from the department with the most hourly workers have complained that their hourly wage is less than $35/hour. Using a one sample t-test, test this one-sided hypothesis at the 95% confidence level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" + "department_hourly_cross = pd.crosstab(data['Department'], data['Salary or Hourly'])\n", + "department_with_most_hourly = department_hourly_cross['Hourly'].idxmax()\n", + "print(\"Department with the most hourly workers:\", department_with_most_hourly)" ] }, { @@ -206,12 +550,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% Confidence Interval for Mean Hourly Wage: (32.52345834488425, 33.05365708767623)\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "mean_hourly_wage = hourly_wage_data['Hourly Rate'].mean()\n", + "std_error = np.std(hourly_wage_data['Hourly Rate'], ddof=1) / np.sqrt(len(hourly_wage_data))\n", + "confidence_level = 0.95\n", + "degrees_of_freedom = len(hourly_wage_data) - 1\n", + "conf_interval = t.interval(confidence_level, degrees_of_freedom, mean_hourly_wage, std_error)\n", + "print(\"95% Confidence Interval for Mean Hourly Wage:\", conf_interval)" ] }, { @@ -223,12 +579,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% Confidence Interval for Mean Annual Salary of Police Salaried Employees: (86177.05631531785, 86795.77269094893)\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "police_salary_data = data[(data['Salary or Hourly'] == 'Salary') & (data['Department'] == 'POLICE')]\n", + "police_salary_data = police_salary_data.dropna(subset=['Annual Salary'])\n", + "mean_annual_salary = police_salary_data['Annual Salary'].mean()\n", + "std_error = np.std(police_salary_data['Annual Salary'], ddof=1) / np.sqrt(len(police_salary_data))\n", + "confidence_level = 0.95\n", + "degrees_of_freedom = len(police_salary_data) - 1\n", + "conf_interval = t.interval(confidence_level, degrees_of_freedom, mean_annual_salary, std_error)\n", + "print(\"95% Confidence Interval for Mean Annual Salary of Police Salaried Employees:\", conf_interval)" ] }, { @@ -246,18 +616,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Null hypothesis rejected: The proportion of hourly workers is significantly different from 25%.\n" + ] + } + ], "source": [ - "# Your code here:\n", - "\n" + "num_hourly_workers = len(data[data['Salary or Hourly'] == 'Hourly'])\n", + "total_employees = len(data)\n", + "proportion_hourly = num_hourly_workers / total_employees\n", + "null_proportion = 0.25 # 25%\n", + "confidence_level = 0.95\n", + "\n", + "\n", + "z_statistic, p_value = sm.stats.proportions_ztest(num_hourly_workers, total_employees, null_proportion, alternative='two-sided')\n", + "alpha = 1 - confidence_level\n", + "if p_value < alpha:\n", + " print(\"Null hypothesis rejected: The proportion of hourly workers is significantly different from 25%.\")\n", + "else:\n", + " print(\"Null hypothesis cannot be rejected: The proportion of hourly workers is not significantly different from 25%.\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -271,7 +660,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,