diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb index a56fb0e..bfb1bfc 100644 --- a/.ipynb_checkpoints/argument-checkpoint.ipynb +++ b/.ipynb_checkpoints/argument-checkpoint.ipynb @@ -10,19 +10,19 @@ }, { "cell_type": "markdown", - "id": "95683a23-56f0-4077-b631-94e456daa8f4", + "id": "b78e413c-505d-4ddf-9af6-ffa72a2f7564", "metadata": {}, "source": [ - "Argument Project Research - Nelson Mason - November 4, 2025" + "# Argument Project Research - Nelson Mason - November 5, 2025" ] }, { "cell_type": "markdown", - "id": "greater-circular", + "id": "ec64e451-1ef8-47e6-9ff3-89b179e86f97", "metadata": {}, "source": [ "## Overarching Question: \n", - "I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.).\n" + "# I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.)\n" ] }, { @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "id": "technical-evans", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "overhead-sigma", "metadata": {}, "outputs": [], @@ -60,10 +60,10 @@ }, { "cell_type": "markdown", - "id": "77e81e19-a1dd-43f1-bf6a-69ffa08add94", + "id": "2af9e676-2cb6-4d48-893e-789d9059f75d", "metadata": {}, "source": [ - "There is a progression of age in terms of the numbers of persons participating in this survey." + "# There is a progression of age in terms of the numbers of persons participating in this survey." ] }, { @@ -99,10 +99,10 @@ }, { "cell_type": "markdown", - "id": "376d050c-1088-414a-9a33-037e9d8a97cc", + "id": "6a37b581-eb94-46fe-b609-59f9d285ca66", "metadata": {}, "source": [ - "Most persons weigh around 75 kgs. in this survey." + "# Most persons weigh around 75 kgs. in this survey." ] }, { @@ -138,10 +138,10 @@ }, { "cell_type": "markdown", - "id": "7fbdae7b-c7c0-472a-a52f-87d158129e25", + "id": "72bbaa32-6802-449b-9d4a-e42124d0f388", "metadata": {}, "source": [ - "There is no individual timeline in this dataset, and therefore no direct relationship between age and weight." + "# There is no individual timeline in this dataset, and therefore no direct relationship between age and weight." ] }, { @@ -177,12 +177,12 @@ }, { "cell_type": "markdown", - "id": "1503d38c-8fe9-4b12-b8f9-ed3c51550c08", + "id": "e853e8c2-5c69-4638-bb10-0d70d3744668", "metadata": {}, "source": [ - "I want to see a distribution of persons by age by weight, with outliers.\n", - "There are many outliers on the upside for all age groups.\n", - "There are very few outliers on the downside for all age groups." + "# I want to see a distribution of persons by age by weight, with outliers.\n", + "# There are many outliers on the upside for all age groups.\n", + "# There are very few outliers on the downside for all age groups." ] }, { @@ -218,10 +218,10 @@ }, { "cell_type": "markdown", - "id": "14aec0ff-b030-4916-8ce3-3ad8c56aa08f", + "id": "a266d2e8-5331-42c0-a787-f1f7f18c9486", "metadata": {}, "source": [ - "I want to see the main distribution of persons by age by weight." + "# I want to see the main distribution of persons by age by weight." ] }, { @@ -275,17 +275,17 @@ }, { "cell_type": "markdown", - "id": "a912ec72-35a8-4325-8520-4a47d806c287", + "id": "0d5ab303-cac5-4327-ab8b-107e4bc1a560", "metadata": {}, "source": [ - "- Do the results give an accurate depiction of your research question? Yes. Why or why not? The dataset is a valid cross-section of U.S. persons. There are 166,426 records in this database. \"This dataset includes data from 50 states, the District of Columbia, Guam, and Puerto Rico, collected through a combination of landline and cell phone interviews.\" - 2020 Behavioral Risk Factor Surveillance System (BRFSS) annual survey data from the Centers for Disease Control and Prevention (CDC).\n", - "- What were limitations of your dataset? The person answering the survey has a telephone, and voluntarily and accurately answers the survey questions.\n", - "- Are there any known biases in the data? No. " + "#### - Do the results give an accurate depiction of your research question? Yes. Why or why not? The dataset is a valid cross-section of U.S. persons. There are 166,426 records in this database. \"This dataset includes data from 50 states, the District of Columbia, Guam, and Puerto Rico, collected through a combination of landline and cell phone interviews.\" - 2020 Behavioral Risk Factor Surveillance System (BRFSS) annual survey data from the Centers for Disease Control and Prevention (CDC).\n", + "#### - What were limitations of your dataset? The person answering the survey has to have a telephone, and voluntarily and accurately answers the survey questions.\n", + "#### - Are there any known biases in the data? No. " ] }, { "cell_type": "markdown", - "id": "4c170ecf-38a3-484b-8188-af144918199d", + "id": "d5170f98-970f-43af-b51c-23cbc9848bf1", "metadata": { "editable": true, "slideshow": { @@ -294,12 +294,20 @@ "tags": [] }, "source": [ - "Conclusions - There is no significant relationship between many persons age and weight because this dataset has no individual timeline. It's only a one-time snapshot." + "## Conclusions" ] }, { "cell_type": "markdown", - "id": "beneficial-invasion", + "id": "bbce0e95-72eb-4bf3-b42d-afbe9d763beb", + "metadata": {}, + "source": [ + "# There is no significant relationship between many persons age and weight because this dataset has no individual timeline. It's only a one-time snapshot." + ] + }, + { + "cell_type": "markdown", + "id": "38771443-d958-4849-a19a-532c58feb13d", "metadata": { "editable": true, "slideshow": { @@ -313,10 +321,10 @@ }, { "cell_type": "markdown", - "id": "b2b17e2e-0d75-4865-ab30-08250f5c8427", + "id": "dc90ca26-d011-4622-b1ee-8954cf81c6dd", "metadata": {}, "source": [ - "I found out that a snapshot dataset cannot discern a direct relationship between many persons age and weight, since there's no timeline in the dataset. What I learned is to be careful in drawing conclusions about datasets based on limited information. " + "### I found out that a snapshot dataset cannot discern a direct relationship between many persons age and weight, since there's no timeline in the dataset. What I learned is to be careful in drawing conclusions about datasets based on limited information. " ] } ], diff --git a/argument.ipynb b/argument.ipynb index a56fb0e..bfb1bfc 100644 --- a/argument.ipynb +++ b/argument.ipynb @@ -10,19 +10,19 @@ }, { "cell_type": "markdown", - "id": "95683a23-56f0-4077-b631-94e456daa8f4", + "id": "b78e413c-505d-4ddf-9af6-ffa72a2f7564", "metadata": {}, "source": [ - "Argument Project Research - Nelson Mason - November 4, 2025" + "# Argument Project Research - Nelson Mason - November 5, 2025" ] }, { "cell_type": "markdown", - "id": "greater-circular", + "id": "ec64e451-1ef8-47e6-9ff3-89b179e86f97", "metadata": {}, "source": [ "## Overarching Question: \n", - "I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.).\n" + "# I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.)\n" ] }, { @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "id": "technical-evans", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "overhead-sigma", "metadata": {}, "outputs": [], @@ -60,10 +60,10 @@ }, { "cell_type": "markdown", - "id": "77e81e19-a1dd-43f1-bf6a-69ffa08add94", + "id": "2af9e676-2cb6-4d48-893e-789d9059f75d", "metadata": {}, "source": [ - "There is a progression of age in terms of the numbers of persons participating in this survey." + "# There is a progression of age in terms of the numbers of persons participating in this survey." ] }, { @@ -99,10 +99,10 @@ }, { "cell_type": "markdown", - "id": "376d050c-1088-414a-9a33-037e9d8a97cc", + "id": "6a37b581-eb94-46fe-b609-59f9d285ca66", "metadata": {}, "source": [ - "Most persons weigh around 75 kgs. in this survey." + "# Most persons weigh around 75 kgs. in this survey." ] }, { @@ -138,10 +138,10 @@ }, { "cell_type": "markdown", - "id": "7fbdae7b-c7c0-472a-a52f-87d158129e25", + "id": "72bbaa32-6802-449b-9d4a-e42124d0f388", "metadata": {}, "source": [ - "There is no individual timeline in this dataset, and therefore no direct relationship between age and weight." + "# There is no individual timeline in this dataset, and therefore no direct relationship between age and weight." ] }, { @@ -177,12 +177,12 @@ }, { "cell_type": "markdown", - "id": "1503d38c-8fe9-4b12-b8f9-ed3c51550c08", + "id": "e853e8c2-5c69-4638-bb10-0d70d3744668", "metadata": {}, "source": [ - "I want to see a distribution of persons by age by weight, with outliers.\n", - "There are many outliers on the upside for all age groups.\n", - "There are very few outliers on the downside for all age groups." + "# I want to see a distribution of persons by age by weight, with outliers.\n", + "# There are many outliers on the upside for all age groups.\n", + "# There are very few outliers on the downside for all age groups." ] }, { @@ -218,10 +218,10 @@ }, { "cell_type": "markdown", - "id": "14aec0ff-b030-4916-8ce3-3ad8c56aa08f", + "id": "a266d2e8-5331-42c0-a787-f1f7f18c9486", "metadata": {}, "source": [ - "I want to see the main distribution of persons by age by weight." + "# I want to see the main distribution of persons by age by weight." ] }, { @@ -275,17 +275,17 @@ }, { "cell_type": "markdown", - "id": "a912ec72-35a8-4325-8520-4a47d806c287", + "id": "0d5ab303-cac5-4327-ab8b-107e4bc1a560", "metadata": {}, "source": [ - "- Do the results give an accurate depiction of your research question? Yes. Why or why not? The dataset is a valid cross-section of U.S. persons. There are 166,426 records in this database. \"This dataset includes data from 50 states, the District of Columbia, Guam, and Puerto Rico, collected through a combination of landline and cell phone interviews.\" - 2020 Behavioral Risk Factor Surveillance System (BRFSS) annual survey data from the Centers for Disease Control and Prevention (CDC).\n", - "- What were limitations of your dataset? The person answering the survey has a telephone, and voluntarily and accurately answers the survey questions.\n", - "- Are there any known biases in the data? No. " + "#### - Do the results give an accurate depiction of your research question? Yes. Why or why not? The dataset is a valid cross-section of U.S. persons. There are 166,426 records in this database. \"This dataset includes data from 50 states, the District of Columbia, Guam, and Puerto Rico, collected through a combination of landline and cell phone interviews.\" - 2020 Behavioral Risk Factor Surveillance System (BRFSS) annual survey data from the Centers for Disease Control and Prevention (CDC).\n", + "#### - What were limitations of your dataset? The person answering the survey has to have a telephone, and voluntarily and accurately answers the survey questions.\n", + "#### - Are there any known biases in the data? No. " ] }, { "cell_type": "markdown", - "id": "4c170ecf-38a3-484b-8188-af144918199d", + "id": "d5170f98-970f-43af-b51c-23cbc9848bf1", "metadata": { "editable": true, "slideshow": { @@ -294,12 +294,20 @@ "tags": [] }, "source": [ - "Conclusions - There is no significant relationship between many persons age and weight because this dataset has no individual timeline. It's only a one-time snapshot." + "## Conclusions" ] }, { "cell_type": "markdown", - "id": "beneficial-invasion", + "id": "bbce0e95-72eb-4bf3-b42d-afbe9d763beb", + "metadata": {}, + "source": [ + "# There is no significant relationship between many persons age and weight because this dataset has no individual timeline. It's only a one-time snapshot." + ] + }, + { + "cell_type": "markdown", + "id": "38771443-d958-4849-a19a-532c58feb13d", "metadata": { "editable": true, "slideshow": { @@ -313,10 +321,10 @@ }, { "cell_type": "markdown", - "id": "b2b17e2e-0d75-4865-ab30-08250f5c8427", + "id": "dc90ca26-d011-4622-b1ee-8954cf81c6dd", "metadata": {}, "source": [ - "I found out that a snapshot dataset cannot discern a direct relationship between many persons age and weight, since there's no timeline in the dataset. What I learned is to be careful in drawing conclusions about datasets based on limited information. " + "### I found out that a snapshot dataset cannot discern a direct relationship between many persons age and weight, since there's no timeline in the dataset. What I learned is to be careful in drawing conclusions about datasets based on limited information. " ] } ], diff --git a/argument_backup.ipynb b/argument_backup.ipynb index 7f7e5f6..ac83ecc 100644 --- a/argument_backup.ipynb +++ b/argument_backup.ipynb @@ -18,11 +18,11 @@ }, { "cell_type": "markdown", - "id": "greater-circular", + "id": "3429fd87-4f14-42e0-a10c-c70a7d0ae192", "metadata": {}, "source": [ "## Overarching Question: \n", - "I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.).\n" + "# I want to know about what relationship exists, if any, between an adult (18 +) person's age and their weight (metric-kg.)\n" ] }, { @@ -91,11 +91,11 @@ }, { "cell_type": "markdown", - "id": "92404f8d-3c67-4d7b-8781-2f8725609eed", + "id": "c44d8b6a-70bf-4e12-aca9-930195496c16", "metadata": {}, "source": [ - "I want to see a count of people by every age group.\n", - "Is this distribution of any significance?" + "# I want to see a count of people by every age group.\n", + "# Is this distribution of any significance?" ] }, { @@ -131,58 +131,34 @@ }, { "cell_type": "markdown", - "id": "363219c6-9a49-4170-bc80-ae5a2b89606e", + "id": "3350be9e-bd7e-4025-b7c5-a84e1ba59c92", "metadata": {}, "source": [ - "I want to see a count of people by weight(kg). Is this distribution of any significance?" + "# I want to see a count of people by weight (kg.) Is this distribution of any significance?" ] }, { - "cell_type": "code", - "execution_count": 18, - "id": "heated-blade", + "cell_type": "markdown", + "id": "f66a904d-aa8e-4e16-b58a-15890e44e4e8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ "sns.barplot(data=people, x=\"age\", y=\"weight\", hue=\"age\")" ] }, { - "cell_type": "markdown", - "id": "continental-franklin", + "cell_type": "raw", + "id": "fb6dfed1-4b5b-4956-b870-aa64e041a045", "metadata": {}, "source": [ - "I'm trying to find out at what age, on average, do people experience a dramatic\n", + "# I'm trying to find out at what age, on average, do people experience a dramatic\n", "weight gain or loss, if at all.\n", - "I'm curious to find out if such a dramatic increase or decrease in weight can\n", - "be captured in a one-time snapshot database, where individuals are NOT tracked \n", + "# I'm curious to find out if such a dramatic increase or decrease in weight can be captured in a one-time snapshot database, where individuals are NOT tracked \n", "over a period of time, but ONLY once." ] }, { - "cell_type": "markdown", - "id": "infinite-instrument", + "cell_type": "raw", + "id": "a75e79d3-f506-41e8-b14f-07ff8f6b3174", "metadata": {}, "source": [ "# Methods and Results"