10th-Grade-Math---USA
Linear-Functions
Easy
Question
Identify the range of following relation
R = {(a, 1), (a, 2), (b, 2), (c, 3)}
- {a, b, c}
- {1, 2, 3}
- {a, 2}
- {a, b, c, 1, 2, 3}
The correct answer is: {1, 2, 3}
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The owner of Horizon flight school created a scatter plot and calculated line of best fit for her enrollment data as shown in the table. The equation of the line of best fit is y = 1.44x + 877. The residuals for 6th year is
![](data:image/png;base64,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)
The owner of Horizon flight school created a scatter plot and calculated line of best fit for her enrollment data as shown in the table. The equation of the line of best fit is y = 1.44x + 877. The residuals for 6th year is
![](data:image/png;base64,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)
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The table shows the number of customers y at a store for x weeks after the store’s grand opening. The equation for the line of best fit is y = 7.77x + 38.8. Assuming the trend continues, the reasonable prediction of the number of visitors to the store 7 weeks after its opening are
![](data:image/png;base64,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)
The table shows the number of customers y at a store for x weeks after the store’s grand opening. The equation for the line of best fit is y = 7.77x + 38.8. Assuming the trend continues, the reasonable prediction of the number of visitors to the store 7 weeks after its opening are
![](data:image/png;base64,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)
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The true statement regarding r = 0.1 is
The true statement regarding r = 0.1 is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
Identify the association from the data in the table
![](data:image/png;base64,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)
Identify the association from the data in the table
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAVsAAABJCAYAAACTpoXJAAAI/ElEQVR4Ae1cS27rOBDUgXIaA9nPdm4RBL6Kc4nsk4NkkWNwQH2pkjSmY3XJ3a4AgdWyYnZ9VKToh9eklNLn52d+0Y8YEANiQAwYMdDkz1XYGrGrjxUDYkAM9AwobGUFMSAGxACBgTFsX15ekn7FgTwgD8gD+3gA83sWtvimx/rt7c1j29U9C181VQ95YXT9tkh/Ntx5wsIfhS0y8uB1dNMK34Mb8I/tRdcVaVHYIiMO6+imFT6HpqxoObquSIHCFhlxWEc3rfA5NGVFy9F1RQoUtsjIHvXPJZ2aJjXvX3t82tXPYJr29+OUmoyt/T0nBkIGvq/3ARO8vl7S71UF7ruAgQ87nOt4SpcfvMK+5uD+TZfXJp2/V/AM92nv59OHrdIK2xUN7jv1lc7NKZ1eo4VtZ1rWBFJqwLkpyxG74xzA1jdgHomNrwvaYqL8Pqem4QeuLe7er32QLsM236dlCGO99MO9Z/hh2wrbrx6GlV85wwzn7kVW/L2tqMVAaRKsXSkZYClHG44p+LJuhFXegKl8peArB8zHrU+LQML3d6y5+LoQmk8ia+d2BLjxURzc0z1ZttFOOOBn63uWH7YZ8Ri43Ww6PsIZhdMRoloLVxqHgS/jOb2WWwjRVu4lo9zwYeg3oVvHxvTr0AsH9/+ELeTNWgAPve7xekzYppTme0aN6arJXtSloEzz2uPrbtD5FkKHeX5uD0suP8MeH4xJXNXmkdn4Wm+SV3XAcFtycC/vzTz42v0ZNmxHwO2eiu1+kbmo5TZIv0fE/BLJHF9aXw1Zm3O4Qe3xDSO1roS9vPI9m2MuvoyhnzzBq/OtBRus5adycK+HbetdrWxLOfY55og673Vt5pxfsV/FwLeGJ2LYsjCV6jP0K8dbHq8H0vK6fc9wcK9jW9N5zeN7Ij5mG2Hcs+2+gGhB5lkWZpq9gHJEnXdrLVw5GgVfv3qfvtXtTMxYDVHwtYSu35gl1xbHPHxr3a9tEa1dt/85Du4tTfE81vvj5YftGLTFP6spH8MNApcj6lyccGGb4RXa5W0SRtDmYVn6tZrBXuZcVZuKhW/qvguWYauLpeM0fndki3t9q2RaLKSUytwh+Jkftsg4obYVlQDgyhDCd4WgB387un5b9D8bboXtlhMcnY9uWuFzZMYbWo2uK1KhsEVGHNbRTSt8Dk1Z0XJ0XZEChS0y4rCOblrhc2jKipaj64oUKGyREYd1dNMKn0NTVrQcXVekQGGLjDiso5tW+ByasqLl6LoiBQpbZMRhHd20wufQlBUtR9cVKVDYIiMO6+imFT6HpqxoObquSIHCFhlxWEc3rfA5NGVFy9F1RQoUtsiIwzq6aYXPoSkrWo6uK1KgsEVGHNbRTSt8Dk1Z0XJ0XZEChS0y4rCOblrhc2jKipaj64oUKGyREYd1dNMKn0NTVrQcXVekQGGLjDiso5tW+ByasqLl6LoiBQpbZMRhHd20wufQlBUtR9cVKVDYIiMO6+imFT6HpqxoObquSIHCFhlxWEc3rfA5NGVFy9F1RQoUtsiIwzq6aYXPoSkrWo6uK1KgsEVGHNbRTSt8Dk1Z0XJ0XZGCq2GbCdGvOJAH5AF54D4PXA1bTGePdTZJ5B/h861udP221Hk23ArbLSc4Oh/dtMLnyIw3tBpdV6RCYYuMOKyjm1b4HJqyouXouiIFCltkxGEd3bTC59CUFS1H1xUpUNgiI3fUvx+n1DRN/3tKl587PuyGP2WY9ut9wAWvr5f0e0Ovf7mUgS+lr3QetSswvn/9peWb/oaDr2jp55JOgPX8XbxPOqTifgDMCtudjNUF7TmNt+b3OTUNJ3Cppi34ygF8+rCO2tT+a5hiWKPDLmzDh05m7/s8160NIo5XS/Govn0AzPSwnVZ/RTCl33R57VcTBisJe1G7/ufBs3autNp+x/b4VnptJ5NSw5VrdjrFwfdEYbvQ5RjsHF0XYPsTfMz0sC0f16ZVRAfcaiVoL+p6sLaP3gaTB9rHHh+OuI4Xr9qr5uAbPDhtIcwnz73QLD+Hg2857niG+BQ2jplYTyzliMXxAZgPCNuUxtXtEEQt8CY1Rvt/DDO3wQr9hw1b4qo23x4M/YrbsDvs9/imBcHiit1OHIIvFfdh0yQGTiTsCNxj9hyA+ZCwTeNmdfcY2oaSIXiOqN1qb/qCrFshMVZHHHzDrcJ//OLiG3CmFHaynCD2R3xN88BH6dqB5mM+JmxTb+Qmf8Fy6b8Fttv/O0ZUnphMfO3KAFbwi3t35xNMfGXrOWzjTZYlwumYhXUa8eiw7TKIoe+A+bCwzd+IlqtAS9D8m7Vf5Q7bJAPbRq88fLwJpKSKgS9PIrNHaeKeHgNfyWcO1hlW4pZJ2QcT9yNgzmF7+uffkoLU5Orz8zOtJfHsyruK8rHb9p+dcETtgmiYQCwnD6Sdg69/GiGvajNWCj6Y/K2+rEXtaPjKgRdYIXzLaw2PKboO/T8A5gPDdtpKaIxXgFRRB3GJr8JHJNtgqOj6bVH2bLiPC9vxSzL7WTW6qMK3dTv7OB9dvy0Vng33YWGb91DaR27CY2l0UYVv63b2cT66flsqPBvuw8J2SwCL89FFFT4L1/A+M7p+W0w+G26F7ZYTHJ2Pblrhc2TGG1qNritSobBFRhzW0U0rfA5NWdFydF2RAoUtMuKwjm5a4XNoyoqWo+uKFChskRGHdXTTCp9DU1a0HF1XpEBhi4w4rKObVvgcmrKi5ei6IgUKW2TEYR3dtMLn0JQVLUfXFSlQ2CIjDuvophU+h6asaDm6rkiBwhYZcVhHN63wOTRlRcvRdUUKFLbIiMM6ummFz6EpK1qOritSoLBFRhzW0U0rfA5NWdFydF2RAoUtMuKwjm5a4XNoyoqWo+uKFChskRGHdXTTCp9DU1a0HF1XpCCHLf6Q/vNwHNauji6q8Nl5h/HJ0fXb4vDZcCtst5zg6Hx00wqfIzPe0Gp0XZEKhS0y4rCOblrhc2jKipaj64oUXA3bfIF+xYE8IA/IA/d7AAN43LPFN1SLATEgBsTAfgwobPfjUp8kBsSAGNhkQGG7SY3eEANiQAzsx8B/Pzv/Zckj7f0AAAAASUVORK5CYII=)
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
If a1 = 2 and common difference is -2, then the 5th term of the sequence is
If a1 = 2 and common difference is -2, then the 5th term of the sequence is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
Write recursive formula for explicit formula an = 8 + 3n.
Write recursive formula for explicit formula an = 8 + 3n.
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
If 10th term of a sequence is 21 and first term is 3, then the common difference of the sequence is
If 10th term of a sequence is 21 and first term is 3, then the common difference of the sequence is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The 21st term of sequence -4, -1, 2, 5, …. is
The 21st term of sequence -4, -1, 2, 5, …. is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The statement among the following that describes the differences between the graph of f and the graph of the output of f multiplied by 3 is
The statement among the following that describes the differences between the graph of f and the graph of the output of f multiplied by 3 is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
Evaluate f(x) = – (x – 2) + 4 for x = 2 and 6.
Evaluate f(x) = – (x – 2) + 4 for x = 2 and 6.
10th-Grade-Math---USALinear-Functions