Introduction to Statistics

STAT 120

Bastola

Let’s do an introduction!

Please fill in! Please fill in!

  • Your name?
  • What gender pronouns do you use?
  • Favorite Scientist/Person?
  • Recent fun memories?

What will you learn in this course?

  • Analyzing data by doing exploratory data analysis
  • Estimate some parameter of interest from the population
  • Infer the population characteristics based in your estimation
  • Quantify the uncertainty in the estimation

What will a typical day/week look like?

Before Class:

  • Some reading to introduce some topics
  • Daily quizzes

What will a typical day/week look like?

During Class:

  • Mini-lectures
  • Hands-on class activities

Statistics is distinct from mathematics

Statistics is the study of data and the uncertainties surrounding them. We will take a more conceptual route to statistics in this course.

What and Why of Statistics?

Science of collecting, describing, analyzing and making decisions based on data

  • Sampling
  • Exploratory Data Analysis
  • Inference

Allows us to make informed decisions in the face of uncertainty and let’s us take an unbiased and evidence-based viewpoint

Statistical Computation

  • Statistical computing software called R
  • RStudio/Posit gives a nice user-friendly interface to R
  • R Markdown is a platform in Posit to write your codes and results

R Documentation

Data: Cases and Variables

Data are a set of measurements taken on a set of individual units

  • These are cases or units


Data is stored and presented in a dataset that comprises of variables measured on cases

  • A variable is any characteristic that is recorded for each case

EducationLiteracy dataset from Lock5

Country Code Education Literacy
Afghanistan AFG 4.23 43.0
Albania ALB 3.95 98.1
Algeria DZA NA 81.4
Andorra AND 3.26 NA
Antigua and Barbuda ATG NA 99.0
Argentina ARG 5.78 99.2
Armenia ARM 2.81 99.7
Aruba ABW 6.48 97.8
Australia AUS 5.32 NA
Austria AUT 5.50 NA
Azerbaijan AZE 2.95 99.8
Bahrain BHR 2.67 97.5
Bangladesh BGD 2.47 73.9
Barbados BRB 5.09 NA
Belarus BLR 4.95 99.8
Belgium BEL 6.54 NA
Belize BLZ 7.38 NA
Benin BEN 4.36 42.4
Bermuda BMU 1.70 NA
Bhutan BTN 7.39 66.6
Bolivia BOL NA 92.5
Brazil BRA 6.24 93.2
British Virgin Islands VGB 6.32 NA
Brunei Darussalam BRN 4.43 97.2
Burkina Faso BFA 4.17 41.2
Burundi BDI 6.37 68.4
Cabo Verde CPV 5.29 86.8
Cambodia KHM NA 80.5
Cameroon CMR 3.07 77.1
Central African Republic CAF NA 37.4
Chad TCD NA 26.0
Chile CHL 5.42 96.9
China CHN NA 96.8
Colombia COL 4.50 95.1
Comoros COM 2.49 58.8
Congo, Dem. Rep. COD 2.17 77.0
Congo, Rep. COG 4.56 80.3
Costa Rica CRI 7.40 97.9
Cote d'Ivoire CIV 5.39 47.2
Cyprus CYP 6.38 NA
Czech Republic CZE 5.79 NA
Dominica DMA 3.39 NA
Dominican Republic DOM NA 93.8
Ecuador ECU 5.00 94.5
Egypt, Arab Rep. EGY NA 71.2
El Salvador SLV 3.96 88.5
Eritrea ERI NA 76.6
Estonia EST 5.17 NA
Eswatini SWZ NA 88.4
Ethiopia ETH 4.74 51.8
Fiji FJI NA 99.1
Finland FIN 7.08 NA
France FRA 5.46 NA
Gabon GAB NA 84.7
Gambia, The GMB 2.06 50.8
Georgia GEO 3.85 99.4
Germany DEU 4.81 NA
Ghana GHA 4.51 79.0
Grenada GRD 10.24 NA
Guatemala GTM 2.96 NA
Guinea GIN 2.52 NA
Guyana GUY 6.34 NA
Haiti HTI 3.16 61.7
Honduras HND 6.41 89.0
Hong Kong SAR, China HKG 3.32 NA
Hungary HUN 4.71 NA
Iceland ISL 7.53 NA
India IND NA 74.4
Indonesia IDN 3.58 95.7
Iran, Islamic Rep. IRN 3.96 85.5
Iraq IRQ NA 50.1
Ireland IRL 3.76 NA
Israel ISR 5.85 NA
Italy ITA 4.08 99.2
Jamaica JAM 5.46 NA
Japan JPN NA NA
Jordan JOR 3.90 98.2
Kazakhstan KAZ 2.98 NA
Kenya KEN 5.36 81.5
Korea, Rep. KOR NA NA
Kuwait KWT NA 96.1
Kyrgyz Republic KGZ 6.59 99.6
Lao PDR LAO NA 84.7
Latvia LVA 5.34 NA
Lebanon LBN NA 95.1
Lesotho LSO 6.51 NA
Liberia LBR NA 48.3
Lithuania LTU 4.22 NA
Luxembourg LUX 3.96 NA
Macao SAR, China MAC 3.08 96.5
Madagascar MDG NA 74.8
Malawi MWI 5.61 62.1
Malaysia MYS 4.97 93.7
Maldives MDV 4.37 97.7
Mali MLI 3.80 35.5
Malta MLT 5.28 94.5
Mauritania MRT 2.63 53.5
Mauritius MUS 5.02 93.2
Mexico MEX 5.24 95.4
Micronesia, Fed. Sts. FSM 12.46 NA
Moldova MDA 6.68 NA
Monaco MCO 1.46 NA
Mongolia MNG 5.18 98.4
Montenegro MNE NA 98.8
Morocco MAR NA 73.8
Mozambique MOZ NA 60.7
Myanmar MMR 2.16 75.6
Nepal NPL 5.16 67.9
Netherlands NLD 5.48 NA
New Zealand NZL 6.43 NA
Nicaragua NIC 4.35 82.6
Niger NER 6.02 NA
Nigeria NGA NA 62.0
Norway NOR 7.98 NA
Oman OMN 6.85 95.7
Pakistan PAK 3.00 59.1
Palau PLW NA 96.6
Panama PAN NA 95.4
Paraguay PRY 3.44 95.6
Peru PER 3.97 94.4
Philippines PHL NA 98.2
Poland POL 4.82 NA
Portugal PRT 4.88 96.1
Puerto Rico PRI NA 92.4
Qatar QAT 2.86 93.5
Romania ROU 3.11 98.8
Russian Federation RUS 3.83 99.7
Rwanda RWA 3.75 73.2
Samoa WSM 4.08 99.1
San Marino SMR 3.04 99.9
Sao Tome and Principe STP 5.08 92.8
Saudi Arabia SAU NA 95.3
Senegal SEN 5.46 51.9
Serbia SRB 4.04 98.8
Seychelles SYC 4.42 95.9
Sierra Leone SLE 4.64 43.2
Singapore SGP NA 97.3
Slovak Republic SVK 4.64 NA
Slovenia SVN 4.91 NA
South Africa ZAF 6.16 94.4
South Sudan SSD 1.50 34.5
Spain ESP 4.27 98.4
Sri Lanka LKA 3.48 92.4
St. Kitts and Nevis KNA 2.61 NA
St. Lucia LCA 5.80 NA
St. Vincent and the Grenadines VCT 5.78 NA
Sudan SDN NA 60.7
Suriname SUR NA 94.4
Sweden SWE 7.67 NA
Switzerland CHE 5.11 NA
Syrian Arab Republic SYR NA NA
Tajikistan TJK 5.23 NA
Tanzania TZA NA 77.9
Thailand THA NA 92.9
Timor-Leste TLS 5.29 68.1
Togo TGO 5.11 63.7
Tonga TON NA 99.4
Tunisia TUN 6.60 NA
Turkey TUR NA 96.2
Turks and Caicos Islands TCA 3.16 NA
Uganda UGA 2.77 76.5
Ukraine UKR 5.41 NA
United Kingdom GBR 5.61 NA
Uruguay URY 4.87 98.7
Uzbekistan UZB 7.08 100.0
Vanuatu VUT 5.55 87.5
Venezuela, RB VEN NA 97.1
Vietnam VNM 4.34 95.0
West Bank and Gaza PSE 5.72 97.2
Zambia ZMB NA 86.7

Each row = case & Each column = variable

Categorical Versus Quantitative

Variables are classified as either categorical or quantitative:


  • A categorical variable divides the cases into groups. e.g. gender, country, state etc.
  • A quantitative variable measures a numerical quantity for each case, e.g. age, height, sleep hours, blood pressure etc

Kidney cancer

Counties with the highest kidney cancer rates


Source: Gelman et. al. Bayesian Data Anaylsis, CRC Press, 2004

Kidney cancer

If the cases in the kidney cancer dataset are people, then the measured variable is categorical

  • We categorize each person as either having kidney cancer or not which is categorical.

Kidney cancer

If the cases in the kidney cancer dataset are counties, then the measured variable is quantitative

  • Data collected at the county level is aggregated across all people living in the county. We then get rates of cancer which are numbers (quantitative).

Variable manipulations

Can use numbers to code categories of categorical variable

  • e.g Gender (1 for male and 2 for female)



Can convert quantitative variable into categorical groups

  • e.g. Income (0-50000 as Low, 50000+ as High)

Explanatory and Response Variable

When one variable helps us understand or predict values of another variable, we call the former the explanatory variable and the latter the response variable

Does meditation help reduce stress?

  • explanatory variable: meditation
  • response variable: stress level

Does sugar consumption increase hyperactivity?

  • explanatory variable: sugar consumption
  • response variable: hyperactive behavior

 Group Activity 1

  • Say hi to your neighbor
  • Make a course folder called stat120 either on your Maize account or on your local computer
  • Please download the Class-Activity-1 template from moodle and go to class helper web page

10:00