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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "312e3496-702e-4446-bf71-f4f0605c2849",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "library(dplyr)\n",
+    "library(ggplot2)\n",
+    "library(corrplot)\n",
+    "library(ggcorrplot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe299820-80b7-4b65-a5f8-7e909f6073ac",
+   "metadata": {},
+   "source": [
+    "#### read data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0cb2745f-0741-4cb7-ac69-894e4167667e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cohort_df = read.csv('~/Downloads/raw-data/cohort.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "49099415-9d24-41de-8743-6cc99012cf17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 5</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>smoke</th><th scope=col>female</th><th scope=col>age</th><th scope=col>cardiac</th><th scope=col>cost</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>0</td><td>1</td><td>48</td><td>0</td><td>9273</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>0</td><td>1</td><td>39</td><td>0</td><td>8636</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>0</td><td>1</td><td>46</td><td>0</td><td>8974</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>0</td><td>0</td><td>43</td><td>0</td><td>9242</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>0</td><td>1</td><td>25</td><td>0</td><td>8404</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>0</td><td>0</td><td>38</td><td>0</td><td>9239</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 5\n",
+       "\\begin{tabular}{r|lllll}\n",
+       "  & smoke & female & age & cardiac & cost\\\\\n",
+       "  & <int> & <int> & <int> & <int> & <int>\\\\\n",
+       "\\hline\n",
+       "\t1 & 0 & 1 & 48 & 0 & 9273\\\\\n",
+       "\t2 & 0 & 1 & 39 & 0 & 8636\\\\\n",
+       "\t3 & 0 & 1 & 46 & 0 & 8974\\\\\n",
+       "\t4 & 0 & 0 & 43 & 0 & 9242\\\\\n",
+       "\t5 & 0 & 1 & 25 & 0 & 8404\\\\\n",
+       "\t6 & 0 & 0 & 38 & 0 & 9239\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 5\n",
+       "\n",
+       "| <!--/--> | smoke &lt;int&gt; | female &lt;int&gt; | age &lt;int&gt; | cardiac &lt;int&gt; | cost &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 1 | 0 | 1 | 48 | 0 | 9273 |\n",
+       "| 2 | 0 | 1 | 39 | 0 | 8636 |\n",
+       "| 3 | 0 | 1 | 46 | 0 | 8974 |\n",
+       "| 4 | 0 | 0 | 43 | 0 | 9242 |\n",
+       "| 5 | 0 | 1 | 25 | 0 | 8404 |\n",
+       "| 6 | 0 | 0 | 38 | 0 | 9239 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  smoke female age cardiac cost\n",
+       "1 0     1      48  0       9273\n",
+       "2 0     1      39  0       8636\n",
+       "3 0     1      46  0       8974\n",
+       "4 0     0      43  0       9242\n",
+       "5 0     1      25  0       8404\n",
+       "6 0     0      38  0       9239"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(cohort_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92f5fdc1-21f8-47fb-bb89-810f55b8a587",
+   "metadata": {},
+   "source": [
+    "#### summarize variable data types, distributions, and statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a50fdcbe-6524-467d-8194-d553b55a3294",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "   0    1 \n",
+       "4211  789 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# variable 'smoke' is a binary variable with values 0 and 1\n",
+    "table(cohort_df$smoke) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8d941c5b-2ccd-4b40-836f-115dd07849ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "   0    1 \n",
+       "2226 2774 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# variable 'female' is a binary variable with values 0 and 1\n",
+    "table(cohort_df$female)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ee8329f4-d2a5-48c2-b432-2de92729ce00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
+       "  18.00   31.00   44.00   43.94   57.00   70.00 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# variable 'age' is a numerical variable with values ranging from 18 to 70\n",
+    "summary(cohort_df$age)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "aaa47680-53dd-42bd-844e-927d971387c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "   0    1 \n",
+       "4750  250 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# variable 'cardiac' is a binary variable with values 0 and 1\n",
+    "table(cohort_df$cardiac)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4d568ad5-9715-43c8-9cf3-571e9f711123",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
+       "   7878    8874    9143    9166    9426   10790 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# variable 'cost' is a numerical variable with values ranging from 7878 to 10790\n",
+    "summary(cohort_df$cost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "a3415006-4c85-4fa2-b236-9169f299aa01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 5</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>smoke</th><th scope=col>female</th><th scope=col>age</th><th scope=col>cardiac</th><th scope=col>cost</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>0</td><td>1</td><td>48</td><td>0</td><td>9273</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>0</td><td>1</td><td>39</td><td>0</td><td>8636</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>0</td><td>1</td><td>46</td><td>0</td><td>8974</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>0</td><td>0</td><td>43</td><td>0</td><td>9242</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>0</td><td>1</td><td>25</td><td>0</td><td>8404</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>0</td><td>0</td><td>38</td><td>0</td><td>9239</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 5\n",
+       "\\begin{tabular}{r|lllll}\n",
+       "  & smoke & female & age & cardiac & cost\\\\\n",
+       "  & <int> & <int> & <int> & <int> & <int>\\\\\n",
+       "\\hline\n",
+       "\t1 & 0 & 1 & 48 & 0 & 9273\\\\\n",
+       "\t2 & 0 & 1 & 39 & 0 & 8636\\\\\n",
+       "\t3 & 0 & 1 & 46 & 0 & 8974\\\\\n",
+       "\t4 & 0 & 0 & 43 & 0 & 9242\\\\\n",
+       "\t5 & 0 & 1 & 25 & 0 & 8404\\\\\n",
+       "\t6 & 0 & 0 & 38 & 0 & 9239\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 5\n",
+       "\n",
+       "| <!--/--> | smoke &lt;int&gt; | female &lt;int&gt; | age &lt;int&gt; | cardiac &lt;int&gt; | cost &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 1 | 0 | 1 | 48 | 0 | 9273 |\n",
+       "| 2 | 0 | 1 | 39 | 0 | 8636 |\n",
+       "| 3 | 0 | 1 | 46 | 0 | 8974 |\n",
+       "| 4 | 0 | 0 | 43 | 0 | 9242 |\n",
+       "| 5 | 0 | 1 | 25 | 0 | 8404 |\n",
+       "| 6 | 0 | 0 | 38 | 0 | 9239 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  smoke female age cardiac cost\n",
+       "1 0     1      48  0       9273\n",
+       "2 0     1      39  0       8636\n",
+       "3 0     1      46  0       8974\n",
+       "4 0     0      43  0       9242\n",
+       "5 0     1      25  0       8404\n",
+       "6 0     0      38  0       9239"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(cohort_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c815882-e8bd-4159-9108-d87ac3ba20ae",
+   "metadata": {},
+   "source": [
+    "#### Examine the correlation between variables\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "582b48fa-7690-452d-8126-640253341b35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A/BHiwd2FakYe17RIHjD5XOl/Lmeam2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\nqY5NhEF7iJ3bgYSVa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+      "text/plain": [
+       "Plot with title “”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cor_matrix <- cor(cohort_df)\n",
+    "corrplot(cor_matrix, \n",
+    "         method = \"color\",    \n",
+    "         type = \"upper\",       \n",
+    "         col = colorRampPalette(c(\"blue\", \"white\", \"red\"))(200),\n",
+    "         tl.cex = 1.5,  \n",
+    "         number.cex = 1,\n",
+    "         tl.col = \"black\")  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb0364ac-8015-40d6-8506-2480893ab5f0",
+   "metadata": {},
+   "source": [
+    "#### Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8ae950f5-5f2e-4f6c-ae3b-ba8979216f4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a model to predict the probability of cardiovascular disease based on multiple variables\n",
+    "model <- glm(cardiac ~ smoke + female + age , data = cohort_df, family = binomial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "20f7ac7f-a774-4b7b-8f84-f10042fd5a9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "glm(formula = cardiac ~ smoke + female + age, family = binomial, \n",
+       "    data = cohort_df)\n",
+       "\n",
+       "Deviance Residuals: \n",
+       "    Min       1Q   Median       3Q      Max  \n",
+       "-0.9702  -0.3345  -0.1100  -0.0983   3.3032  \n",
+       "\n",
+       "Coefficients:\n",
+       "             Estimate Std. Error z value Pr(>|z|)    \n",
+       "(Intercept) -3.186602   0.236180 -13.492   <2e-16 ***\n",
+       "smoke        2.111761   0.141071  14.969   <2e-16 ***\n",
+       "female      -2.466895   0.203725 -12.109   <2e-16 ***\n",
+       "age          0.008083   0.004676   1.729   0.0839 .  \n",
+       "---\n",
+       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
+       "\n",
+       "(Dispersion parameter for binomial family taken to be 1)\n",
+       "\n",
+       "    Null deviance: 1985.2  on 4999  degrees of freedom\n",
+       "Residual deviance: 1547.2  on 4996  degrees of freedom\n",
+       "AIC: 1555.2\n",
+       "\n",
+       "Number of Fisher Scoring iterations: 7\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "summary(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "ae853373-1134-4a89-aa5a-8790dd74a2de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.2627792344367"
+      ],
+      "text/latex": [
+       "8.2627792344367"
+      ],
+      "text/markdown": [
+       "8.2627792344367"
+      ],
+      "text/plain": [
+       "[1] 8.262779"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.0848479031534758"
+      ],
+      "text/latex": [
+       "0.0848479031534758"
+      ],
+      "text/markdown": [
+       "0.0848479031534758"
+      ],
+      "text/plain": [
+       "[1] 0.0848479"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "1.00811575563963"
+      ],
+      "text/latex": [
+       "1.00811575563963"
+      ],
+      "text/markdown": [
+       "1.00811575563963"
+      ],
+      "text/plain": [
+       "[1] 1.008116"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute odds based on regression results\n",
+    "# For 'smoke': smoking increases the odds of cardiovascular disease by a factor of 8.263\n",
+    "exp(2.111761) \n",
+    "# For 'female': being a female decreases the odds of cardiovascular disease by a factor of 0.085\n",
+    "exp(-2.466895) \n",
+    "# For 'age': one unit increase in age increases the odds of cardiovascular disease by a factor of 1.008\n",
+    "exp(0.008083) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "187aad05-fb79-4353-8177-567b4a10e727",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.2.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analysis/ReadMe.md b/analysis/ReadMe.md
new file mode 100644
index 0000000..6174490
--- /dev/null
+++ b/analysis/ReadMe.md
@@ -0,0 +1,6 @@
+Analysis findings:
+
+In this assignment, I built a logistic regression model using the cohort.csv data to predict the probability of cardiovascular disease based on age, gender, and smoking status (whether the individual is a smoker or not). The results show that all three variables are meaningfully associated with the disease. Smoking and being female are significantly correlated with the disease. Being a smoker increases the likelihood of the disease and being a female decreases the likelihood. Both of these two factors have a p-value less than 0.05, indicating strong statistical significance. Age is also positively related to the disease, indicating older population has higher likelihood of the disease comparing to younger population, however it shows borderline significance. 
+
+I did not use generative AI technology (e.g., ChatGPT) to complete any portion of the work. 
+